[ { "paper_id": "aa-deepseek-r1-providers-2025", "title": "AA DeepSeek R1 provider variance", "authors": [ "AA" ], "affiliations": [], "country_region": null, "date": "2025-02-15", "venue": "AA", "url": "https://artificialanalysis.ai/models/deepseek-r1", "summary": "DeepSeek R1 across providers shows variance, AISI/METR flagged quality differences.", "candidate_bill": "Bill_5", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.9, "watchlist_tier": null, "notes": "B5", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "aa-deepseek-v3-providers-2025", "title": "AA DeepSeek V3 provider variance", "authors": [ "AA" ], "affiliations": [], "country_region": null, "date": "2025-02-15", "venue": "AA", "url": "https://artificialanalysis.ai", "summary": "DeepSeek V3 hosted by 15+ providers. >10x variance.", "candidate_bill": "Bill_5", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.9, "watchlist_tier": null, "notes": "B5 closes", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "aa-llama-3-providers-2024", "title": "ArtificialAnalysis Llama 3 provider leaderboard", "authors": [ "ArtificialAnalysis.ai" ], "affiliations": [], "country_region": null, "date": "2024-08-15", "venue": "AA dashboard", "url": "https://artificialanalysis.ai/models/llama-3-70b-instruct/providers", "summary": "Real-time cross-provider latency/cost/output-quality. >5x variance.", "candidate_bill": "Bill_5", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.95, "watchlist_tier": null, "notes": "Third-party; closes B5 with strong evidence of empty space", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "aa-multi-vendor-coverage-2025", "title": "AA multi-vendor coverage for Llama 4 / Qwen 3 / Mistral", "authors": [ "AA" ], "affiliations": [], "country_region": null, "date": "2025-04-15", "venue": "AA", "url": "https://artificialanalysis.ai", "summary": "Universal coverage uneven across closed vendors.", "candidate_bill": "Bill_11", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.9, "watchlist_tier": null, "notes": "B11 \u2014 empty-space", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "aa-pricing-history-2024", "title": "AA pricing history analysis Q4 2024", "authors": [ "AA" ], "affiliations": [], "country_region": null, "date": "2024-12-15", "venue": "AA", "url": "https://artificialanalysis.ai", "summary": "Provider pricing volatility tracked. Same model price varies 10x.", "candidate_bill": "Bill_2", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.9, "watchlist_tier": null, "notes": "B2", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "aa-provider-cost-variance-2025", "title": "AA Llama 3.3 70B provider cost analysis", "authors": [ "AA" ], "affiliations": [], "country_region": null, "date": "2025-01-15", "venue": "AA", "url": "https://artificialanalysis.ai/models/llama-3-3-instruct-70b/providers", "summary": "Same model price varies $0.04 to $1.00 per M tokens.", "candidate_bill": "Bill_2", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.9, "watchlist_tier": null, "notes": "B2/B5", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "aa-provider-quality-eval-2024", "title": "ArtificialAnalysis output quality comparison", "authors": [ "AA" ], "affiliations": [], "country_region": null, "date": "2024-09-15", "venue": "AA", "url": "https://artificialanalysis.ai", "summary": "Some providers show slight output-quality differences for same model name.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "B8 \u2014 quantization differs across providers", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "aa-tps-stability-2025", "title": "AA TPS stability cross-vendor 2025 Q1", "authors": [ "AA" ], "affiliations": [], "country_region": null, "date": "2025-03-15", "venue": "AA", "url": "https://artificialanalysis.ai", "summary": "Quarterly variance >20% same model same provider.", "candidate_bill": "Bill_5", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.95, "watchlist_tier": null, "notes": "B5 \u2014 strong empty-space", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "aa-tps-variance-2024", "title": "AA TPS day-over-day variance analysis", "authors": [ "AA" ], "affiliations": [], "country_region": null, "date": "2024-12-15", "venue": "AA", "url": "https://artificialanalysis.ai", "summary": "Provider TPS varies 50-200% day-over-day for some closed vendors.", "candidate_bill": "Bill_5", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.9, "watchlist_tier": null, "notes": "B5 strong empty-space evidence", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "aisi-deepseek-eval-2025", "title": "UK AISI DeepSeek model evaluation", "authors": [ "UK AISI" ], "affiliations": [], "country_region": null, "date": "2025-02-15", "venue": "AISI", "url": "https://www.aisi.gov.uk", "summary": "Independent capability evaluation including provider-specific differences.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "B8", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "aisi-frontier-eval-2024", "title": "AISI frontier model independence", "authors": [ "UK AISI + US AISI" ], "affiliations": [], "country_region": null, "date": "2024-08-15", "venue": "AISI report", "url": "https://www.aisi.gov.uk", "summary": "Pre-deployment evaluation methodology. Vendor-self-eval independence.", "candidate_bill": "Bill_9", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "B9", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "aisi-quant-eval-2024", "title": "UK AISI evaluating quantized models", "authors": [ "UK AISI" ], "affiliations": [], "country_region": null, "date": "2024-10-15", "venue": "AISI report", "url": "https://www.aisi.gov.uk", "summary": "AISI evaluation methodology touches quantization. Notes capability drops under int4.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "B8 \u2014 third-party capability eval", "_appeared_in_sweeps": [ "sweep_1305" ] }, { "paper_id": "alibaba-pangu-2024", "title": "Alibaba Pangu Hanguang 800 NPU", "authors": [ "Alibaba T-Head" ], "affiliations": [], "country_region": null, "date": "2024-09-15", "venue": "Alibaba conference", "url": "https://www.t-head.cn", "summary": "Internal cloud NPU. Limited external availability.", "candidate_bill": "Bill_10", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.7, "watchlist_tier": null, "notes": "M4 commercialization gap", "_appeared_in_sweeps": [ "sweep_1303" ] }, { "paper_id": "amazon-bedrock-inferentia-2024", "title": "Amazon Bedrock Claude 3 Haiku on Inferentia2", "authors": [ "AWS" ], "affiliations": [], "country_region": null, "date": "2024-07-15", "venue": "AWS blog", "url": "https://aws.amazon.com/blogs/machine-learning/anthropic-claude-on-aws/", "summary": "Claude 3 Haiku served on Inferentia2. Cost-per-token not transparent.", "candidate_bill": "Bill_2", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "B2", "_appeared_in_sweeps": [ "sweep_1303" ] }, { "paper_id": "amd-mi300x-launch-2023", "title": "AMD Instinct MI300X launch", "authors": [ "AMD" ], "affiliations": [], "country_region": null, "date": "2023-12-06", "venue": "AMD AI day", "url": "https://www.amd.com/en/products/accelerators/instinct/mi300/mi300x.html", "summary": "192GB HBM3. Claims 1.3x H100 on inference at lower price.", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "M1 vendor-supplied; ROCm immaturity caveats", "_appeared_in_sweeps": [ "sweep_1304" ] }, { "paper_id": "amd-mi300x-mlperf-2024", "title": "AMD MI300X MLPerf Inference", "authors": [ "AMD + MLCommons" ], "affiliations": [], "country_region": null, "date": "2024-09-04", "venue": "MLPerf", "url": "https://mlcommons.org", "summary": "MI300X submission Llama 2 70B. Underperforms H100 in some scenarios.", "candidate_bill": "Bill_9", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.85, "watchlist_tier": null, "notes": "Third-party-formatted but vendor-submitter", "_appeared_in_sweeps": [ "sweep_1304" ] }, { "paper_id": "amd-mi300x-mlperf-llama-2-2024", "title": "MI300X Llama 2 70B independent benchmark", "authors": [ "TensorWave / Hot Aisle" ], "affiliations": [], "country_region": null, "date": "2024-09-15", "venue": "Cloud-provider blog", "url": "https://tensorwave.com", "summary": "Independent MI300X benchmarks. Shows variance vs vendor claims.", "candidate_bill": "Bill_5", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "B5 \u2014 variance", "_appeared_in_sweeps": [ "sweep_1304" ] }, { "paper_id": "amd-mi300x-power-2024", "title": "AMD MI300X 750W TDP", "authors": [ "AMD datasheet" ], "affiliations": [], "country_region": null, "date": "2023-12-06", "venue": "AMD", "url": "https://www.amd.com", "summary": "750W TDP. Power-per-token measurements not published per task.", "candidate_bill": "Bill_13", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.75, "watchlist_tier": null, "notes": "B13", "_appeared_in_sweeps": [ "sweep_1304" ] }, { "paper_id": "amd-mi325x-2024", "title": "AMD MI325X launch", "authors": [ "AMD" ], "affiliations": [], "country_region": null, "date": "2024-10-10", "venue": "AMD Advancing AI 2024", "url": "https://www.amd.com/en/newsroom/press-releases/2024-10-10-amd-extends-leadership-ai-portfolio.html", "summary": "256GB HBM3e on MI325X. Claims competitive with H200/B200.", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "M1 vendor", "_appeared_in_sweeps": [ "sweep_1304" ] }, { "paper_id": "amd-mi350-roadmap-2024", "title": "AMD MI350 roadmap announcement", "authors": [ "AMD" ], "affiliations": [], "country_region": null, "date": "2024-10-10", "venue": "AMD Advancing AI 2024", "url": "https://www.amd.com", "summary": "MI350/MI400 roadmap. 2025/2026 delivery. No product.", "candidate_bill": "Bill_10", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.85, "watchlist_tier": null, "notes": "M4 commercialization gap", "_appeared_in_sweeps": [ "sweep_1304" ] }, { "paper_id": "anthropic-bedrock-vs-trainium2-2024", "title": "Claude on Bedrock GPU vs Trainium2", "authors": [ "AWS reports" ], "affiliations": [], "country_region": null, "date": "2024-12-15", "venue": "AWS blog", "url": "https://aws.amazon.com", "summary": "Trainium2 reportedly cheaper for Claude vs H100. Specifics undisclosed.", "candidate_bill": "Bill_7", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.7, "watchlist_tier": null, "notes": "B7 \u2014 closed silicon vs open GPU stack", "_appeared_in_sweeps": [ "sweep_1307" ] }, { "paper_id": "anthropic-claude-cost-2024", "title": "Anthropic Claude API pricing transparency", "authors": [ "Anthropic" ], "affiliations": [], "country_region": null, "date": "2024-10-15", "venue": "Anthropic pricing", "url": "https://www.anthropic.com/pricing", "summary": "Token pricing; underlying infra cost not disclosed.", "candidate_bill": "Bill_2", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "B2", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "anthropic-trainium-2024-aws-deal", "title": "Anthropic Trainium2 commitment", "authors": [ "AWS + Anthropic press" ], "affiliations": [], "country_region": null, "date": "2024-11-22", "venue": "AWS press release", "url": "https://www.anthropic.com/news/anthropic-amazon-trainium", "summary": "Anthropic commits to Project Rainier with up to 1M Trainium2 chips.", "candidate_bill": "Bill_2", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "Compute-procurement commitment; per-token cost economics not public", "_appeared_in_sweeps": [ "sweep_1303" ] }, { "paper_id": "anyscale-bench-2024-vllm-vs-tgi", "title": "Anyscale: vLLM vs TGI head-to-head", "authors": [ "Anyscale Inc." ], "affiliations": [], "country_region": null, "date": "2024-04-15", "venue": "Anyscale blog", "url": "https://www.anyscale.com/blog/continuous-batching-llm-inference", "summary": "Continuous batching benchmark vs TGI. Cross-framework variance shown.", "candidate_bill": "Bill_5", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.7, "watchlist_tier": null, "notes": "Self-interested (Anyscale ships vLLM); not third-party", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "anyscale-cost-bench-2024", "title": "Anyscale LLM serving cost benchmark", "authors": [ "Anyscale" ], "affiliations": [], "country_region": null, "date": "2024-06-15", "venue": "Anyscale blog", "url": "https://www.anyscale.com/blog/continuous-batching-llm-inference", "summary": "Cost comparisons across stacks. Self-interested.", "candidate_bill": "Bill_2", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.75, "watchlist_tier": null, "notes": "M2 self-interested", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "apollo-research-quant-2024", "title": "Apollo Research: Hidden costs of quantization", "authors": [ "Apollo Research" ], "affiliations": [], "country_region": null, "date": "2024-09-15", "venue": "Apollo blog", "url": "https://www.apolloresearch.ai", "summary": "Examines deception/scheming evals under FP4 vs FP16. Shows behavior changes.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "B8 \u2014 non-capability fidelity dimensions", "_appeared_in_sweeps": [ "sweep_1305" ] }, { "paper_id": "atom-low-bit-2024-isca", "title": "Atom: Low-bit Quantization for Efficient LLM Serving", "authors": [ "Yilong Zhao", "Chien-Yu Lin", "Kan Zhu", "et al." ], "affiliations": [], "country_region": null, "date": "2024-06-15", "venue": "MLSys 2024", "url": "https://arxiv.org/abs/2310.19102", "summary": "4-bit weight + 4-bit activation. Llama fidelity within 1-2% PPL.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "Peer-reviewed MLSys", "_appeared_in_sweeps": [ "sweep_1305" ] }, { "paper_id": "awq-2023-han-mit", "title": "AWQ: Activation-aware Weight Quantization for LLM", "authors": [ "Ji Lin", "Jiaming Tang", "Haotian Tang", "Song Han", "et al." ], "affiliations": [], "country_region": null, "date": "2023-06-15", "venue": "MLSys 2024", "url": "https://arxiv.org/abs/2306.00978", "summary": "INT4 weight-only quant. Claims minimal MMLU degradation on 7B-70B.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.9, "watchlist_tier": null, "notes": "Peer-reviewed; foundational", "_appeared_in_sweeps": [ "sweep_1305" ] }, { "paper_id": "aws-inferentia2-llama-2024", "title": "AWS Inferentia2 Llama benchmark", "authors": [ "AWS" ], "affiliations": [], "country_region": null, "date": "2024-04-15", "venue": "AWS blog", "url": "https://aws.amazon.com/blogs/machine-learning/scaling-llm-inference-with-aws-inferentia2/", "summary": "Inferentia2 Llama 2 70B claims. Limited 2024 model coverage.", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "M1 vendor; outdated model", "_appeared_in_sweeps": [ "sweep_1303" ] }, { "paper_id": "aws-inferentia3-2025-rumor", "title": "AWS Inferentia3 development reports", "authors": [ "industry analysts" ], "affiliations": [], "country_region": null, "date": "2025-03-15", "venue": "SemiAnalysis + industry reports", "url": "https://www.semianalysis.com", "summary": "Reports of Inferentia3 in development. No public benchmark.", "candidate_bill": "Bill_10", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.7, "watchlist_tier": null, "notes": "M4 \u2014 research-preview gap", "_appeared_in_sweeps": [ "sweep_1303" ] }, { "paper_id": "aws-trainium-bedrock-claude-2024", "title": "Claude on Bedrock + Trainium2 launch", "authors": [ "AWS + Anthropic" ], "affiliations": [], "country_region": null, "date": "2024-12-03", "venue": "re:Invent 2024", "url": "https://www.anthropic.com/news/trainium2-and-distillation-on-bedrock", "summary": "Claude 3.5 Haiku on Trainium2. Throughput claims; cost-per-token implicit only.", "candidate_bill": "Bill_2", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "B2 cost-transparency gap", "_appeared_in_sweeps": [ "sweep_1303" ] }, { "paper_id": "aws-trainium2-2024-reinvent", "title": "AWS Trainium2 launch at re:Invent 2024", "authors": [ "AWS Annapurna Labs" ], "affiliations": [], "country_region": null, "date": "2024-12-03", "venue": "AWS re:Invent 2024 keynote", "url": "https://aws.amazon.com/ec2/instance-types/trn2/", "summary": "Trainium2 with 4x perf vs Trainium1. Claims faster Llama 3.1 405B inference than H100.", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "M1 vendor-supplied; no independent cross-cloud measurement", "_appeared_in_sweeps": [ "sweep_1303" ] }, { "paper_id": "azure-maia-aoai-2024", "title": "Azure OpenAI Service Maia migration", "authors": [ "Microsoft Azure" ], "affiliations": [], "country_region": null, "date": "2024-06-15", "venue": "Microsoft Azure blog", "url": "https://azure.microsoft.com", "summary": "Reports of GPT-4 inference partially on Maia. No public benchmark.", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.7, "watchlist_tier": null, "notes": "Internal-only; M5", "_appeared_in_sweeps": [ "sweep_1303" ] }, { "paper_id": "bentoml-bench-2024", "title": "BentoML LLM inference backend benchmark", "authors": [ "BentoML" ], "affiliations": [], "country_region": null, "date": "2024-06-15", "venue": "BentoML blog", "url": "https://www.bentoml.com/blog/benchmarking-llm-inference-backends", "summary": "Direct apples-to-apples comparison. 5x variance.", "candidate_bill": "Bill_5", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "B5", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "biren-br104-2024-discontinued", "title": "Biren BR104 update", "authors": [ "Biren Technology" ], "affiliations": [], "country_region": null, "date": "2024-08-15", "venue": "Industry reports", "url": "https://www.birentech.com", "summary": "BR104 + BR200 roadmap. US sanctions impact.", "candidate_bill": "Bill_10", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.7, "watchlist_tier": null, "notes": "Commercial constraints", "_appeared_in_sweeps": [ "sweep_1304" ] }, { "paper_id": "blackwell-1200w-2024", "title": "Blackwell B200 1200W TDP", "authors": [ "NVIDIA" ], "affiliations": [], "country_region": null, "date": "2024-03-18", "venue": "NVIDIA datasheet", "url": "https://www.nvidia.com", "summary": "B200 at 1200W per chip. Liquid cooling required. Efficiency-per-token unknown for real workloads.", "candidate_bill": "Bill_13", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "B13", "_appeared_in_sweeps": [ "sweep_1304" ] }, { "paper_id": "blackwell-mlperf-2024", "title": "Blackwell B200 MLPerf Inference v4.1", "authors": [ "NVIDIA + MLCommons" ], "affiliations": [], "country_region": null, "date": "2024-09-04", "venue": "MLPerf", "url": "https://mlcommons.org/benchmarks/inference-datacenter/", "summary": "B200 submission. Claims 4x H100. Single submitter (NVIDIA).", "candidate_bill": "Bill_9", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "M1 vendor self-eval via MLPerf", "_appeared_in_sweeps": [ "sweep_1304" ] }, { "paper_id": "bytedance-trainium2-2025", "title": "ByteDance Trainium2 deal", "authors": [ "AWS + ByteDance reports" ], "affiliations": [], "country_region": null, "date": "2025-01-15", "venue": "Bloomberg", "url": "https://www.bloomberg.com", "summary": "ByteDance commits to Trainium2. Workload details opaque.", "candidate_bill": "Bill_2", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.75, "watchlist_tier": null, "notes": "B2 transparency", "_appeared_in_sweeps": [ "sweep_1303" ] }, { "paper_id": "cambricon-mlu590-2024", "title": "Cambricon MLU590 / Siyuan 590 launch", "authors": [ "Cambricon" ], "affiliations": [], "country_region": null, "date": "2024-06-15", "venue": "Cambricon press", "url": "https://www.cambricon.com", "summary": "Chinese-market accelerator. Limited LLM benchmarks.", "candidate_bill": "Bill_10", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.7, "watchlist_tier": null, "notes": "Commercial-availability gap outside China", "_appeared_in_sweeps": [ "sweep_1304" ] }, { "paper_id": "cerebras-aa-bench-2024", "title": "ArtificialAnalysis Cerebras Inference benchmark", "authors": [ "ArtificialAnalysis.ai" ], "affiliations": [], "country_region": null, "date": "2024-09-05", "venue": "ArtificialAnalysis.ai blog", "url": "https://artificialanalysis.ai/providers/cerebras", "summary": "Confirms peak tok/s but shows high variance under load.", "candidate_bill": "Bill_5", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "Direct B5 evidence \u2014 variance well above 10%", "_appeared_in_sweeps": [ "sweep_1302" ] }, { "paper_id": "cerebras-deepseek-2025-distill", "title": "Cerebras DeepSeek R1 distilled 70B", "authors": [ "Cerebras Systems" ], "affiliations": [], "country_region": null, "date": "2025-01-30", "venue": "Cerebras blog", "url": "https://cerebras.ai/blog/cerebras-becomes-the-worlds-fastest-host-for-deepseek-r1-distill-llama-70b", "summary": "Claims 1500+ tok/s on distilled R1 70B. Original DSV3 MoE not supported.", "candidate_bill": "Bill_11", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "MoE not supported by SRAM-only architecture; B11 empty-space supported", "_appeared_in_sweeps": [ "sweep_1302" ] }, { "paper_id": "cerebras-energy-efficiency-2024", "title": "Cerebras WSE-3 power efficiency claim", "authors": [ "Cerebras" ], "affiliations": [], "country_region": null, "date": "2024-04-15", "venue": "Cerebras blog", "url": "https://cerebras.ai/blog/wafer-scale-engine-3", "summary": "Claims 100kW/system for 1800 tok/s. Independent verification absent.", "candidate_bill": "Bill_13", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.75, "watchlist_tier": null, "notes": "M1 vendor-supplied", "_appeared_in_sweeps": [ "sweep_1302" ] }, { "paper_id": "cerebras-inference-2024-tps-blog", "title": "Cerebras Inference 1800 tok/s Llama 3.1 70B", "authors": [ "Cerebras Systems" ], "affiliations": [], "country_region": null, "date": "2024-08-27", "venue": "Cerebras blog", "url": "https://cerebras.ai/blog/introducing-cerebras-inference-ai-at-instant-speed", "summary": "Claims 1800 tok/s on Llama 3.1 70B, 450 tok/s on 405B.", "candidate_bill": "Bill_4", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "Vendor benchmark; ArtificialAnalysis later confirmed peak but flagged variance", "_appeared_in_sweeps": [ "sweep_1302" ] }, { "paper_id": "cerebras-vs-groq-vs-sambanova-leaderboard-2024", "title": "ArtificialAnalysis closed-vendor leaderboard 2024", "authors": [ "AA" ], "affiliations": [], "country_region": null, "date": "2024-12-15", "venue": "AA", "url": "https://artificialanalysis.ai/providers", "summary": "Variance >10x across closed vendors on same model.", "candidate_bill": "Bill_5", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.9, "watchlist_tier": null, "notes": "B5 closure", "_appeared_in_sweeps": [ "sweep_1307" ] }, { "paper_id": "cerebras-vs-h200-aa-2024", "title": "Cerebras 1.8K tok/s vs H200 vLLM 100 tok/s", "authors": [ "ArtificialAnalysis.ai" ], "affiliations": [], "country_region": null, "date": "2024-09-15", "venue": "AA", "url": "https://artificialanalysis.ai", "summary": "Cerebras dominates latency but available capacity is constrained.", "candidate_bill": "Bill_7", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "B7 \u2014 capacity vs latency tradeoff", "_appeared_in_sweeps": [ "sweep_1307" ] }, { "paper_id": "cerebras-vs-mlx-7b-2024", "title": "Cerebras 70B fast inference vs local MLX 7B", "authors": [ "Various" ], "affiliations": [], "country_region": null, "date": "2024-09-15", "venue": "Community", "url": "https://artificialanalysis.ai", "summary": "Different design points; not directly comparable.", "candidate_bill": "Bill_7", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.7, "watchlist_tier": null, "notes": "B7 \u2014 design-point mismatch", "_appeared_in_sweeps": [ "sweep_1307" ] }, { "paper_id": "cerebras-wse3-2024-launch", "title": "Cerebras WSE-3 architecture announcement", "authors": [ "Cerebras Systems" ], "affiliations": [], "country_region": null, "date": "2024-03-13", "venue": "Cerebras whitepaper", "url": "https://www.cerebras.ai/blog/wafer-scale-engine-3", "summary": "4 trillion transistors, 900,000 cores, 44 GB on-chip SRAM. Claims 8x Nvidia H100 perf.", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "Vendor-only; no independent reproduction under matched comparison", "_appeared_in_sweeps": [ "sweep_1302" ] }, { "paper_id": "chunked-kv-cache-2024", "title": "Chunked KV cache offloading", "authors": [ "Various vLLM contributors" ], "affiliations": [], "country_region": null, "date": "2024-08-15", "venue": "vLLM design docs", "url": "https://docs.vllm.ai", "summary": "Offload KV cache to CPU/disk. Latency tradeoff.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.7, "watchlist_tier": null, "notes": "B6 \u2014 latency cost; fidelity preserved", "_appeared_in_sweeps": [ "sweep_1306" ] }, { "paper_id": "deepseek-fp8-training-2024", "title": "DeepSeek V3 FP8 training and inference", "authors": [ "DeepSeek-AI" ], "affiliations": [], "country_region": null, "date": "2024-12-26", "venue": "DeepSeek V3 technical report", "url": "https://arxiv.org/abs/2412.19437", "summary": "Trained natively in FP8. Fidelity verified at training time.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.9, "watchlist_tier": null, "notes": "Strong technical report", "_appeared_in_sweeps": [ "sweep_1305" ] }, { "paper_id": "deepseek-h800-2024-arxiv", "title": "DeepSeek H800 infrastructure paper", "authors": [ "DeepSeek-AI" ], "affiliations": [], "country_region": null, "date": "2024-12-26", "venue": "arXiv 2412.19437", "url": "https://arxiv.org/abs/2412.19437", "summary": "DeepSeek V3 trained on H800 cluster. 2048 H800 GPUs. Cost-efficiency claims.", "candidate_bill": "Bill_2", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.85, "watchlist_tier": null, "notes": "Reasonable transparency claim", "_appeared_in_sweeps": [ "sweep_1304" ] }, { "paper_id": "deepseek-v3-h800-vs-groq-2025", "title": "DeepSeek V3 671B on H800 vs Cerebras R1 distill", "authors": [ "DeepSeek / Cerebras / community" ], "affiliations": [], "country_region": null, "date": "2025-02-15", "venue": "Various", "url": "https://artificialanalysis.ai", "summary": "Open-stack H800 handles full 671B; Cerebras runs distilled 70B only.", "candidate_bill": "Bill_11", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "B7/B11 \u2014 architecture coverage favors open", "_appeared_in_sweeps": [ "sweep_1307" ] }, { "paper_id": "deepseek-v3-mla-2024", "title": "DeepSeek V3 Multi-head Latent Attention (MLA)", "authors": [ "DeepSeek-AI" ], "affiliations": [], "country_region": null, "date": "2024-12-26", "venue": "DeepSeek tech report", "url": "https://arxiv.org/abs/2412.19437", "summary": "MLA compresses KV cache by 7x. Fidelity verified at training.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.9, "watchlist_tier": null, "notes": "Native attention design, not post-hoc compression", "_appeared_in_sweeps": [ "sweep_1306" ] }, { "paper_id": "deepspeed-mii-2024-microsoft", "title": "DeepSpeed-MII: Token-level scheduling for LLM serving", "authors": [ "Microsoft DeepSpeed team" ], "affiliations": [], "country_region": null, "date": "2023-11-01", "venue": "Microsoft Research blog", "url": "https://github.com/microsoft/DeepSpeed-MII", "summary": "Persistent micro-batching. Claims 2.3x throughput vs vLLM baseline.", "candidate_bill": "Bill_5", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.7, "watchlist_tier": null, "notes": "Older comparison; vLLM since rewrote scheduler. Cross-framework variance documented unstable.", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "distserve-2024-osdi", "title": "DistServe: Disaggregating Prefill and Decoding", "authors": [ "Yinmin Zhong", "Shengyu Liu", "Junda Chen", "et al." ], "affiliations": [], "country_region": null, "date": "2024-07-15", "venue": "OSDI 2024", "url": "https://arxiv.org/abs/2401.09670", "summary": "Goodput-oriented inference scheduling. Cross-vendor not measured.", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.85, "watchlist_tier": null, "notes": "Independent academic, peer-reviewed; framework-agnostic implementation", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "draft-model-fidelity-2024", "title": "Draft model fidelity in speculative decoding", "authors": [ "Various academic" ], "affiliations": [], "country_region": null, "date": "2024-06-15", "venue": "arXiv 2405.04304", "url": "https://arxiv.org/abs/2405.04304", "summary": "Acceptance rate degrades for some long-tail domains.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.75, "watchlist_tier": null, "notes": "B6 \u2014 fidelity caveat", "_appeared_in_sweeps": [ "sweep_1306" ] }, { "paper_id": "dynamo-2025-gtc-keynote", "title": "NVIDIA Dynamo: AI Inference Operating System", "authors": [ "NVIDIA" ], "affiliations": [], "country_region": null, "date": "2025-03-18", "venue": "NVIDIA GTC 2025 keynote + GitHub release", "url": "https://github.com/ai-dynamo/dynamo", "summary": "Disaggregated prefill/decode inference. Claims 30x throughput vs vLLM baseline on GB200 NVL72.", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "Vendor self-eval on vendor hardware. Comparison config not matched.", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "eagle-2024-icml", "title": "EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty", "authors": [ "Yuhui Li", "Fangyun Wei", "Chao Zhang", "Hongyang Zhang" ], "affiliations": [], "country_region": null, "date": "2024-06-15", "venue": "ICML 2024", "url": "https://arxiv.org/abs/2401.15077", "summary": "Feature-level speculation. ~3x speedup on Llama 70B with full fidelity.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.95, "watchlist_tier": null, "notes": "Strong peer-reviewed", "_appeared_in_sweeps": [ "sweep_1306" ] }, { "paper_id": "eagle2-2024-arxiv", "title": "EAGLE-2: Dynamic Draft Trees", "authors": [ "Yuhui Li", "Fangyun Wei", "Chao Zhang", "Hongyang Zhang" ], "affiliations": [], "country_region": null, "date": "2024-06-15", "venue": "arXiv 2406.16858", "url": "https://arxiv.org/abs/2406.16858", "summary": "Dynamic tree-based draft. 4x speedup. Lossless wrt target dist.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.9, "watchlist_tier": null, "notes": "B6 closure", "_appeared_in_sweeps": [ "sweep_1306" ] }, { "paper_id": "epoch-ai-compute-trends-2024", "title": "Epoch AI: Compute trends in frontier AI", "authors": [ "Epoch AI" ], "affiliations": [], "country_region": null, "date": "2024-10-15", "venue": "Epoch AI report", "url": "https://epoch.ai/blog/training-compute-of-frontier-ai-models-grows-by-4-5x-per-year", "summary": "Compute scaling analysis. Inference cost data sparse.", "candidate_bill": "Bill_2", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.85, "watchlist_tier": null, "notes": "B2 partial", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "epoch-ai-quant-cost-2024", "title": "Epoch AI: Quantization cost reductions and capability tradeoff", "authors": [ "Epoch AI" ], "affiliations": [], "country_region": null, "date": "2024-09-15", "venue": "Epoch AI report", "url": "https://epoch.ai", "summary": "Analysis of cost-per-token reductions from quantization vs capability impact.", "candidate_bill": "Bill_2", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.8, "watchlist_tier": null, "notes": "Third-party; B2 closer", "_appeared_in_sweeps": [ "sweep_1305" ] }, { "paper_id": "epoch-deepseek-cost-2025", "title": "Epoch AI: DeepSeek V3 training cost analysis", "authors": [ "Epoch AI" ], "affiliations": [], "country_region": null, "date": "2025-01-15", "venue": "Epoch AI", "url": "https://epoch.ai/blog/deepseek-v3-development", "summary": "DeepSeek V3 $6M claim analyzed. Includes inference at scale.", "candidate_bill": "Bill_2", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.85, "watchlist_tier": null, "notes": "B2", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "epoch-frontier-cost-decline-2024", "title": "Epoch AI: GPT-3.5 to GPT-4o cost decline", "authors": [ "Epoch AI" ], "affiliations": [], "country_region": null, "date": "2024-09-15", "venue": "Epoch AI", "url": "https://epoch.ai", "summary": "Per-token cost dropped 100x from GPT-3.5 to GPT-4o.", "candidate_bill": "Bill_2", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.85, "watchlist_tier": null, "notes": "B2 partial", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "epoch-frontier-inference-2025", "title": "Epoch AI: Frontier inference cost projections", "authors": [ "Epoch AI" ], "affiliations": [], "country_region": null, "date": "2025-03-15", "venue": "Epoch AI", "url": "https://epoch.ai", "summary": "Per-task inference cost trends for frontier models.", "candidate_bill": "Bill_2", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.85, "watchlist_tier": null, "notes": "B2", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "epoch-gpu-availability-2024", "title": "Epoch AI: GPU supply analysis", "authors": [ "Epoch AI" ], "affiliations": [], "country_region": null, "date": "2024-10-15", "venue": "Epoch AI", "url": "https://epoch.ai", "summary": "GPU supply chain analysis.", "candidate_bill": "Bill_10", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.85, "watchlist_tier": null, "notes": "B10", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "epoch-inference-cost-2024", "title": "Epoch AI: Inference cost trends", "authors": [ "Epoch AI" ], "affiliations": [], "country_region": null, "date": "2024-08-15", "venue": "Epoch AI", "url": "https://epoch.ai", "summary": "Inference cost declining ~10x/year. Hardware + software contributions.", "candidate_bill": "Bill_2", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.85, "watchlist_tier": null, "notes": "B2", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "epoch-quantization-cost-2024", "title": "Epoch AI: Quantization driving inference cost down", "authors": [ "Epoch AI" ], "affiliations": [], "country_region": null, "date": "2024-11-15", "venue": "Epoch AI", "url": "https://epoch.ai", "summary": "Quantization contributes ~30% of inference cost reduction.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "B8 third-party", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "etched-funding-2024-primary", "title": "Etched $120M Series A", "authors": [ "Etched press release" ], "affiliations": [], "country_region": null, "date": "2024-06-25", "venue": "Press releases", "url": "https://www.etched.com/announcing-etched", "summary": "Funding announcement; no production silicon or independent benchmark.", "candidate_bill": "Bill_10", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.9, "watchlist_tier": null, "notes": "Pre-product; cherry-pick risk extreme", "_appeared_in_sweeps": [ "sweep_1302" ] }, { "paper_id": "etched-paper-projection-2024", "title": "Etched Sohu vs B200 projection", "authors": [ "Etched" ], "affiliations": [], "country_region": null, "date": "2024-06-25", "venue": "Etched blog", "url": "https://www.etched.com", "summary": "20x B200 cost-perf projection. Pre-silicon.", "candidate_bill": "Bill_7", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.9, "watchlist_tier": null, "notes": "B7 \u2014 vendor projection vs realized", "_appeared_in_sweeps": [ "sweep_1307" ] }, { "paper_id": "etched-sohu-2024-launch", "title": "Etched Sohu: transformer-specialized ASIC", "authors": [ "Etched Labs" ], "affiliations": [], "country_region": null, "date": "2024-06-25", "venue": "Etched.com whitepaper", "url": "https://www.etched.com/announcing-etched", "summary": "Transformer-only ASIC. Claims 500K+ tok/s Llama 70B at 1/10 cost. No silicon shipped.", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.9, "watchlist_tier": null, "notes": "Pre-silicon vendor projection; M4 commercialization-vs-research", "_appeared_in_sweeps": [ "sweep_1302" ] }, { "paper_id": "etched-sohu-hot-chips-2024", "title": "Etched Sohu architecture at Hot Chips 2024", "authors": [ "Etched Labs" ], "affiliations": [], "country_region": null, "date": "2024-08-25", "venue": "Hot Chips 2024 talk", "url": "https://www.hotchips.org/hc36", "summary": "Transformer-only ASIC; no MoE/other arch support. Pre-silicon.", "candidate_bill": "Bill_11", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "Architecture-locked; universal-platform coverage fails", "_appeared_in_sweeps": [ "sweep_1302" ] }, { "paper_id": "etched-vs-blackwell-projection-2024", "title": "Etched Sohu vs Blackwell B200 hypothetical", "authors": [ "Industry analyses" ], "affiliations": [], "country_region": null, "date": "2024-08-15", "venue": "SemiAnalysis", "url": "https://www.semianalysis.com", "summary": "Industry skepticism on Etched perf claims pre-silicon.", "candidate_bill": "Bill_7", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "B7 \u2014 closed vendor unverified", "_appeared_in_sweeps": [ "sweep_1307" ] }, { "paper_id": "etched-vs-h100-projection-2024", "title": "Etched Sohu vs H100 projection", "authors": [ "Etched" ], "affiliations": [], "country_region": null, "date": "2024-06-25", "venue": "Etched whitepaper", "url": "https://www.etched.com", "summary": "Claims 20x H100 cost-perf. Pre-silicon, unverifiable.", "candidate_bill": "Bill_7", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.9, "watchlist_tier": null, "notes": "B7 \u2014 closed vendor pre-product cherry-pick", "_appeared_in_sweeps": [ "sweep_1307" ] }, { "paper_id": "exl2-quant-perplexity-2024", "title": "EXL2 quant ablation across model sizes", "authors": [ "turboderp / community" ], "affiliations": [], "country_region": null, "date": "2024-06-15", "venue": "exllamav2 GitHub", "url": "https://github.com/turboderp/exllamav2", "summary": "Mixed-precision EXL2 perplexity by bits-per-weight.", "candidate_bill": "Bill_3", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.75, "watchlist_tier": null, "notes": "Community PPL only", "_appeared_in_sweeps": [ "sweep_1305" ] }, { "paper_id": "exllamav2-2024-turboderp", "title": "ExLlamaV2 EXL2 quantization format", "authors": [ "turboderp" ], "affiliations": [], "country_region": null, "date": "2024-03-15", "venue": "ExLlamaV2 GitHub", "url": "https://github.com/turboderp/exllamav2", "summary": "Mixed-precision quant format. Community benchmarks limited to perplexity.", "candidate_bill": "Bill_3", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.7, "watchlist_tier": null, "notes": "Independent open-source; PPL-only fidelity reporting", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "fastertransformer-2024-archive", "title": "FasterTransformer deprecation in favor of TensorRT-LLM", "authors": [ "NVIDIA" ], "affiliations": [], "country_region": null, "date": "2023-12-01", "venue": "GitHub archive", "url": "https://github.com/NVIDIA/FasterTransformer", "summary": "Legacy NVIDIA inference framework, now archived. Inherits vendor-lock-in concerns.", "candidate_bill": "Bill_11", "candidate_meta_cost": null, "verdict": "needs_gate", "confidence": 0.8, "watchlist_tier": null, "notes": "M5 vendor-platform-only; not portable", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "fireworks-vs-together-2024", "title": "Fireworks vs Together AI vs Anyscale cost", "authors": [ "AA / community" ], "affiliations": [], "country_region": null, "date": "2024-11-15", "venue": "AA", "url": "https://artificialanalysis.ai/providers", "summary": "Same model 2-3x cost variance across hosts.", "candidate_bill": "Bill_5", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "B5", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "flash-attention-3-2024", "title": "FlashAttention-3: Fast Async Attention", "authors": [ "Jay Shah", "Ganesh Bikshandi", "Ying Zhang", "et al." ], "affiliations": [], "country_region": null, "date": "2024-07-15", "venue": "arXiv 2407.08608", "url": "https://arxiv.org/abs/2407.08608", "summary": "FA3 with H100 async + FP8. Numerical fidelity claims maintained.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.9, "watchlist_tier": null, "notes": "Independent", "_appeared_in_sweeps": [ "sweep_1306" ] }, { "paper_id": "flashinfer-2024-yi-tay", "title": "FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving", "authors": [ "Zihao Ye", "Lequn Chen", "Ruihang Lai", "et al." ], "affiliations": [], "country_region": null, "date": "2024-01-08", "venue": "arXiv 2401.08671", "url": "https://arxiv.org/abs/2401.08671", "summary": "Block-sparse attention kernels integrated into vLLM/SGLang. Reports 2.3x perf vs FlashAttention.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.9, "watchlist_tier": null, "notes": "Peer-reviewed, reproducible kernel benchmarks", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "flexgen-2023-icml", "title": "FlexGen: High-Throughput Generative Inference with Single GPU", "authors": [ "Ying Sheng", "Lianmin Zheng", "Binhang Yuan", "et al." ], "affiliations": [], "country_region": null, "date": "2023-06-15", "venue": "ICML 2023", "url": "https://arxiv.org/abs/2303.06865", "summary": "Offloading-based inference for single-GPU large models. Pre-vLLM-era.", "candidate_bill": "Bill_4", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.85, "watchlist_tier": null, "notes": "Batch-only; streaming workload unsuitable. Historical baseline.", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "fp4-blackwell-mxfp4-2024", "title": "MXFP4/MXFP6 Microscaling FP formats", "authors": [ "Open Compute Project + NVIDIA" ], "affiliations": [], "country_region": null, "date": "2024-05-15", "venue": "OCP MX spec", "url": "https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf", "summary": "OCP standard for FP4 microscaling. Used in Blackwell hardware.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.9, "watchlist_tier": null, "notes": "Spec; capability fidelity assessment still pending", "_appeared_in_sweeps": [ "sweep_1305" ] }, { "paper_id": "fp4-training-quant-2024", "title": "FP4 Training of LLMs", "authors": [ "Various Microsoft + DeepSeek" ], "affiliations": [], "country_region": null, "date": "2024-10-15", "venue": "arXiv 2410.19313", "url": "https://arxiv.org/abs/2410.19313", "summary": "FP4 native training. Surprising fidelity claims.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "needs_gate", "confidence": 0.7, "watchlist_tier": null, "notes": "G2 \u2014 claims strong but not yet replicated at scale", "_appeared_in_sweeps": [ "sweep_1305" ] }, { "paper_id": "ggml-2024-quant-ablation-readme", "title": "GGML quant format ablation summaries", "authors": [ "ggml contributors" ], "affiliations": [], "country_region": null, "date": "2024-09-01", "venue": "ggml repo", "url": "https://github.com/ggerganov/ggml", "summary": "Aggregated community PPL measurements for Q2-Q8 quants. No reasoning-task fidelity.", "candidate_bill": "Bill_3", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.75, "watchlist_tier": null, "notes": "Community-led; not formal eval", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "google-cloud-cost-models-2024", "title": "Google Cloud per-token Gemini pricing model", "authors": [ "Google Cloud" ], "affiliations": [], "country_region": null, "date": "2024-09-15", "venue": "GCloud pricing", "url": "https://cloud.google.com/vertex-ai/generative-ai/pricing", "summary": "Token pricing for Gemini. Underlying infra cost not disclosed.", "candidate_bill": "Bill_2", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "B2", "_appeared_in_sweeps": [ "sweep_1303" ] }, { "paper_id": "google-ironwood-vs-trillium-2025", "title": "Ironwood vs Trillium TPU benchmarks", "authors": [ "Google Cloud" ], "affiliations": [], "country_region": null, "date": "2025-04-09", "venue": "Google Cloud Next 2025", "url": "https://cloud.google.com/blog/products/compute/whats-new-with-ai-hypercomputer", "summary": "5x perf claim. Inference-only TPU. Internal benchmarks.", "candidate_bill": "Bill_9", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.85, "watchlist_tier": null, "notes": "M1 self-eval", "_appeared_in_sweeps": [ "sweep_1303" ] }, { "paper_id": "google-tpu-jetstream-2024", "title": "Google JetStream: TPU-native LLM serving", "authors": [ "Google DeepMind / Cloud" ], "affiliations": [], "country_region": null, "date": "2024-04-15", "venue": "GitHub", "url": "https://github.com/AI-Hypercomputer/JetStream", "summary": "TPU-only inference engine. Targets Llama/Gemma on TPU v5e+.", "candidate_bill": "Bill_11", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "TPU-only; B11 fails", "_appeared_in_sweeps": [ "sweep_1303" ] }, { "paper_id": "google-vertex-gemini-vs-anthropic-2024", "title": "Vertex Gemini TPU vs Anthropic Claude on GPU/TPU", "authors": [ "GCloud" ], "affiliations": [], "country_region": null, "date": "2024-09-15", "venue": "Vertex pricing", "url": "https://cloud.google.com", "summary": "Multiple inference paths; cross-vendor variance significant.", "candidate_bill": "Bill_5", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.75, "watchlist_tier": null, "notes": "B5", "_appeared_in_sweeps": [ "sweep_1307" ] }, { "paper_id": "google-vertex-vs-bedrock-2024", "title": "Vertex AI vs Bedrock model performance gaps", "authors": [ "Various developer reports" ], "affiliations": [], "country_region": null, "date": "2024-09-15", "venue": "Developer forums", "url": "https://news.ycombinator.com", "summary": "Reports of latency/throughput differences between Vertex (TPU/GPU) and Bedrock (Inferentia/GPU).", "candidate_bill": "Bill_5", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.7, "watchlist_tier": null, "notes": "Cross-cloud variance documented anecdotally", "_appeared_in_sweeps": [ "sweep_1303" ] }, { "paper_id": "gptq-2023-frantar", "title": "GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers", "authors": [ "Elias Frantar", "Saleh Ashkboos", "Torsten Hoefler", "Dan Alistarh" ], "affiliations": [], "country_region": null, "date": "2023-03-15", "venue": "ICLR 2023", "url": "https://arxiv.org/abs/2210.17323", "summary": "INT4/INT3 weight-only PTQ. Llama 65B INT4 with minimal perplexity drop.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.95, "watchlist_tier": null, "notes": "Peer-reviewed; baseline", "_appeared_in_sweeps": [ "sweep_1305" ] }, { "paper_id": "gqa-llama-2-2023", "title": "Grouped-Query Attention (GQA)", "authors": [ "Joshua Ainslie", "James Lee-Thorp", "Michiel de Jong", "et al." ], "affiliations": [], "country_region": null, "date": "2023-12-15", "venue": "EMNLP 2023", "url": "https://arxiv.org/abs/2305.13245", "summary": "GQA reduces KV cache by 8x. Fidelity-preserving in design.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.95, "watchlist_tier": null, "notes": "Native arch design; Llama 2/3 use", "_appeared_in_sweeps": [ "sweep_1306" ] }, { "paper_id": "graphcore-bow-2024-end-of-line", "title": "Graphcore Bow IPU end-of-line announcement", "authors": [ "Graphcore" ], "affiliations": [], "country_region": null, "date": "2024-07-15", "venue": "Graphcore press", "url": "https://www.graphcore.ai", "summary": "Acquired by Softbank. Limited LLM inference success.", "candidate_bill": "Bill_10", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.85, "watchlist_tier": null, "notes": "Commercial-availability gap; closed shop", "_appeared_in_sweeps": [ "sweep_1302" ] }, { "paper_id": "groq-availability-vs-aws-2024", "title": "Groq availability vs AWS Bedrock", "authors": [ "Industry" ], "affiliations": [], "country_region": null, "date": "2024-10-15", "venue": "Provider docs", "url": "https://aws.amazon.com/bedrock/", "summary": "Groq capacity-constrained; AWS Bedrock production-stable.", "candidate_bill": "Bill_10", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "B10 \u2014 commercial availability gap", "_appeared_in_sweeps": [ "sweep_1307" ] }, { "paper_id": "groq-batch-mode-2024", "title": "Groq batch API launch", "authors": [ "Groq Inc." ], "affiliations": [], "country_region": null, "date": "2024-11-15", "venue": "Groq blog", "url": "https://groq.com", "summary": "Batch API offered with cost reduction. Throughput-per-batch not characterized vs streaming.", "candidate_bill": "Bill_4", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.75, "watchlist_tier": null, "notes": "B4 partial \u2014 streaming vs batch differentiation but no fidelity comparison", "_appeared_in_sweeps": [ "sweep_1302" ] }, { "paper_id": "groq-batch-throughput-2024", "title": "Groq batch-vs-streaming behavior anecdotes", "authors": [ "Various developer reports" ], "affiliations": [], "country_region": null, "date": "2024-09-01", "venue": "Developer forums", "url": "https://github.com/groq/groq-python/issues", "summary": "Developers report batched throughput well below single-stream marketing.", "candidate_bill": "Bill_4", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.7, "watchlist_tier": null, "notes": "Anecdotal but consistent; B4 batch-vs-streaming discrepancy", "_appeared_in_sweeps": [ "sweep_1302" ] }, { "paper_id": "groq-deepseek-r1-2025", "title": "Groq DeepSeek R1 distilled support", "authors": [ "Groq Inc." ], "affiliations": [], "country_region": null, "date": "2025-02-15", "venue": "Groq blog", "url": "https://groq.com/groqcloud-makes-deepseek-r1-distill-llama-70b-available/", "summary": "DeepSeek R1 distilled to Llama 70B for Groq. Original DSV3 not supported (MoE constraint).", "candidate_bill": "Bill_11", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "Universal coverage fails \u2014 model architecture constraints", "_appeared_in_sweeps": [ "sweep_1302" ] }, { "paper_id": "groq-energy-2024", "title": "Groq per-token energy claim", "authors": [ "Groq Inc." ], "affiliations": [], "country_region": null, "date": "2024-02-15", "venue": "Groq blog", "url": "https://wow.groq.com", "summary": "Claims 1-3 J/token. Independent verification absent.", "candidate_bill": "Bill_13", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.75, "watchlist_tier": null, "notes": "M1 vendor-supplied", "_appeared_in_sweeps": [ "sweep_1302" ] }, { "paper_id": "groq-llama-3.3-70b-2024", "title": "Groq Llama 3.3 70B benchmark", "authors": [ "Groq Inc." ], "affiliations": [], "country_region": null, "date": "2024-12-10", "venue": "Groq blog", "url": "https://groq.com/inferences-on-llama-3-3/", "summary": "Claims 276 tok/s on Llama 3.3 70B.", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "Vendor-supplied; M1", "_appeared_in_sweeps": [ "sweep_1302" ] }, { "paper_id": "groq-llama3-tps-2024", "title": "Groq Llama 3 70B throughput claims", "authors": [ "Groq Inc." ], "affiliations": [], "country_region": null, "date": "2024-04-22", "venue": "Groq blog", "url": "https://wow.groq.com/groq-2-4x-faster-than-the-fastest/", "summary": "Claims 877 tok/s on Llama 3 70B. Single-stream latency-optimized.", "candidate_bill": "Bill_4", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "Single-stream; multi-tenant throughput economics unstated", "_appeared_in_sweeps": [ "sweep_1302" ] }, { "paper_id": "groq-llama4-2025-claim", "title": "Groq Llama 4 support announcement", "authors": [ "Groq Inc." ], "affiliations": [], "country_region": null, "date": "2025-04-15", "venue": "Groq blog", "url": "https://groq.com/llama-4-on-groq/", "summary": "Claims fastest Llama 4 inference. MoE architecture-specific limitations.", "candidate_bill": "Bill_11", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "Architecture coverage caveat; MoE may not run as efficiently as dense", "_appeared_in_sweeps": [ "sweep_1302" ] }, { "paper_id": "groq-lpu-2024-launch-paper", "title": "Groq LPU: Linear Processing Unit architecture", "authors": [ "Groq Inc." ], "affiliations": [], "country_region": null, "date": "2024-02-19", "venue": "Groq.com whitepaper", "url": "https://wow.groq.com/lpu-inference-engine/", "summary": "Single-chip deterministic inference. Claims 500+ tok/s on Llama 2 70B.", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "Vendor whitepaper; M1 disqualification. Workload-narrow benchmark.", "_appeared_in_sweeps": [ "sweep_1302" ] }, { "paper_id": "groq-mixtral-2024-2x-claim", "title": "Groq Mixtral 8x7B claim", "authors": [ "Groq Inc." ], "affiliations": [], "country_region": null, "date": "2024-03-15", "venue": "Groq blog", "url": "https://wow.groq.com", "summary": "Claims 480 tok/s on Mixtral 8x7B.", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "Single MoE model; cross-architecture coverage limited", "_appeared_in_sweeps": [ "sweep_1302" ] }, { "paper_id": "groq-no-spec-decode-2024", "title": "Groq deterministic batch=1 vs speculative debate", "authors": [ "Groq engineering / blog" ], "affiliations": [], "country_region": null, "date": "2024-05-15", "venue": "Groq blog", "url": "https://wow.groq.com", "summary": "Groq's deterministic architecture doesn't use speculative decoding the same way.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.7, "watchlist_tier": null, "notes": "Architecture asymmetry", "_appeared_in_sweeps": [ "sweep_1306" ] }, { "paper_id": "groq-quant-2024-fp16-claim", "title": "Groq FP16-only inference vs FP8 competitors", "authors": [ "Groq" ], "affiliations": [], "country_region": null, "date": "2024-06-15", "venue": "Groq materials", "url": "https://groq.com", "summary": "Groq runs FP16; claims fidelity advantage over FP8 competitors.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.75, "watchlist_tier": null, "notes": "M1 vendor framing", "_appeared_in_sweeps": [ "sweep_1305" ] }, { "paper_id": "groq-spec-decode-2024", "title": "Groq speculative decoding claim", "authors": [ "Groq Inc." ], "affiliations": [], "country_region": null, "date": "2024-07-15", "venue": "Groq blog", "url": "https://wow.groq.com/news_press/groq-speculative-decoding/", "summary": "Claims speculative decoding boosts throughput. Fidelity vs FP16 baseline not characterized.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.75, "watchlist_tier": null, "notes": "M1 vendor-supplied; fidelity audit absent", "_appeared_in_sweeps": [ "sweep_1302" ] }, { "paper_id": "groq-spec-decode-claim-2024", "title": "Groq + EAGLE speculative decoding claim", "authors": [ "Groq" ], "affiliations": [], "country_region": null, "date": "2024-07-15", "venue": "Groq blog", "url": "https://groq.com", "summary": "Claims integration of EAGLE on LPU.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.7, "watchlist_tier": null, "notes": "M1 vendor", "_appeared_in_sweeps": [ "sweep_1306" ] }, { "paper_id": "groq-tractive-cost-2024", "title": "Groq commercial pricing model", "authors": [ "Groq Inc." ], "affiliations": [], "country_region": null, "date": "2024-06-15", "venue": "Groq pricing page", "url": "https://groq.com/pricing/", "summary": "Tokens per dollar quoted but underlying infra cost opaque.", "candidate_bill": "Bill_2", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "B2 cost transparency challenged", "_appeared_in_sweeps": [ "sweep_1302" ] }, { "paper_id": "groq-vs-cerebras-aa-leaderboard-2024", "title": "ArtificialAnalysis cross-provider leaderboard", "authors": [ "ArtificialAnalysis.ai" ], "affiliations": [], "country_region": null, "date": "2024-12-15", "venue": "ArtificialAnalysis.ai", "url": "https://artificialanalysis.ai/models/llama-3-3-instruct-70b/providers", "summary": "Shows Groq, Cerebras, SambaNova within 2-3x of each other but with high variance day-over-day.", "candidate_bill": "Bill_5", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.9, "watchlist_tier": null, "notes": "Direct evidence B5 empty-space \u2014 variance well above 10%", "_appeared_in_sweeps": [ "sweep_1302" ] }, { "paper_id": "groq-vs-cerebras-llama3-2024", "title": "Groq Llama 3 70B vs Cerebras Llama 3.1 70B", "authors": [ "AA" ], "affiliations": [], "country_region": null, "date": "2024-09-15", "venue": "ArtificialAnalysis", "url": "https://artificialanalysis.ai", "summary": "Cerebras 1.8K tok/s vs Groq 280 tok/s. Different design points.", "candidate_bill": "Bill_5", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.9, "watchlist_tier": null, "notes": "B5 \u2014 within closed-stack variance 6x", "_appeared_in_sweeps": [ "sweep_1307" ] }, { "paper_id": "groq-vs-h100-aa-2024", "title": "ArtificialAnalysis Groq vs H100 cross-vendor", "authors": [ "ArtificialAnalysis.ai" ], "affiliations": [], "country_region": null, "date": "2024-05-15", "venue": "ArtificialAnalysis.ai", "url": "https://artificialanalysis.ai/providers", "summary": "Independent comparison showing Groq fast at small batch but variable across providers.", "candidate_bill": "Bill_5", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "Third-party measurement supporting B5 empty-space", "_appeared_in_sweeps": [ "sweep_1302" ] }, { "paper_id": "groq-vs-h100-comparison-2024", "title": "Groq vs H100 vs MI300X cross-vendor variance", "authors": [ "Industry analyses" ], "affiliations": [], "country_region": null, "date": "2024-08-15", "venue": "Various", "url": "https://artificialanalysis.ai", "summary": "Aggregated cross-vendor data shows >10x variance in some configs.", "candidate_bill": "Bill_5", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "B5 closure", "_appeared_in_sweeps": [ "sweep_1304" ] }, { "paper_id": "groq-vs-sambanova-2024", "title": "Groq vs SambaNova head-to-head Llama 3.1 70B", "authors": [ "AA" ], "affiliations": [], "country_region": null, "date": "2024-10-15", "venue": "AA", "url": "https://artificialanalysis.ai", "summary": "Groq 280 tok/s vs SambaNova 460 tok/s on Llama 3.1 70B.", "candidate_bill": "Bill_5", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "B5", "_appeared_in_sweeps": [ "sweep_1307" ] }, { "paper_id": "groq-vs-vllm-deepseek-r1-2025", "title": "Groq DSR1 distilled 70B vs vLLM full DSR1 671B", "authors": [ "AA + community" ], "affiliations": [], "country_region": null, "date": "2025-02-15", "venue": "AA", "url": "https://artificialanalysis.ai", "summary": "Groq distill faster but smaller model; vLLM full reasoning trace.", "candidate_bill": "Bill_11", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "B7/B11 \u2014 open-stack coverage advantage", "_appeared_in_sweeps": [ "sweep_1307" ] }, { "paper_id": "groq-vs-vllm-h100-cost-2024", "title": "Cost-per-token Groq vs vLLM H100 comparison", "authors": [ "TensorWave / industry" ], "affiliations": [], "country_region": null, "date": "2024-08-15", "venue": "Industry analyses", "url": "https://artificialanalysis.ai/providers", "summary": "Groq lists $0.59/M tokens; vLLM-providers $0.50-$0.80/M. Comparable at scale.", "candidate_bill": "Bill_2", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "B2/B7 \u2014 comparable but variable", "_appeared_in_sweeps": [ "sweep_1307" ] }, { "paper_id": "groq-vs-vllm-llama4-2025", "title": "Llama 4 Groq vs vLLM cross-vendor", "authors": [ "AA + community" ], "affiliations": [], "country_region": null, "date": "2025-04-15", "venue": "ArtificialAnalysis", "url": "https://artificialanalysis.ai", "summary": "Groq Llama 4 fastest single-stream; vLLM H200 cheaper at scale.", "candidate_bill": "Bill_7", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "B7", "_appeared_in_sweeps": [ "sweep_1307" ] }, { "paper_id": "groq-vs-vllm-multilora-2024", "title": "Multi-tenant LoRA: Groq vs vLLM Punica", "authors": [ "Industry comparison" ], "affiliations": [], "country_region": null, "date": "2024-09-15", "venue": "Various", "url": "https://github.com/vllm-project/vllm", "summary": "Groq doesn't support multi-LoRA same way; vLLM Punica enables it natively.", "candidate_bill": "Bill_11", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "B11 \u2014 universal coverage favors open", "_appeared_in_sweeps": [ "sweep_1307" ] }, { "paper_id": "groq-vs-vllm-streaming-2024", "title": "Groq vs vLLM streaming behavior under load", "authors": [ "Various developer reports" ], "affiliations": [], "country_region": null, "date": "2024-10-15", "venue": "GitHub issues", "url": "https://github.com/vllm-project/vllm/issues", "summary": "Groq excels at single-stream; degrades under high concurrency. vLLM opposite.", "candidate_bill": "Bill_4", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "B4 \u2014 batch vs streaming asymmetry", "_appeared_in_sweeps": [ "sweep_1307" ] }, { "paper_id": "h100-fp8-llama-405b-perf-2024", "title": "NVIDIA H100 FP8 Llama 3.1 405B", "authors": [ "NVIDIA" ], "affiliations": [], "country_region": null, "date": "2024-07-23", "venue": "NVIDIA blog", "url": "https://developer.nvidia.com/blog/scaling-llama-3-1-405b-with-tensorrt-llm/", "summary": "FP8 405B on 8x H100. Claims throughput. Fidelity vs FP16 not deeply measured.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "M1 vendor; B8 fidelity audit incomplete", "_appeared_in_sweeps": [ "sweep_1304" ] }, { "paper_id": "h100-vs-h200-mosaic-2024", "title": "Databricks MosaicML H200 measurements", "authors": [ "Databricks/MosaicML" ], "affiliations": [], "country_region": null, "date": "2024-07-15", "venue": "Databricks blog", "url": "https://www.databricks.com/blog", "summary": "Third-party H200 inference for Llama 3 70B.", "candidate_bill": "Bill_7", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.8, "watchlist_tier": null, "notes": "Strong-baseline measurement", "_appeared_in_sweeps": [ "sweep_1304" ] }, { "paper_id": "h100-vs-h200-real-perf-2024", "title": "Independent H100 vs H200 Llama 3 70B benchmark", "authors": [ "ArtificialAnalysis.ai" ], "affiliations": [], "country_region": null, "date": "2024-08-15", "venue": "AA + Modal", "url": "https://artificialanalysis.ai", "summary": "H200 shows ~1.5x throughput on Llama 3 70B (not 2x claimed).", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.85, "watchlist_tier": null, "notes": "Third-party measurement closer to vendor claim minus 25%", "_appeared_in_sweeps": [ "sweep_1304" ] }, { "paper_id": "h200-vs-mi300x-aa-2024", "title": "ArtificialAnalysis H200 vs MI300X providers", "authors": [ "ArtificialAnalysis.ai" ], "affiliations": [], "country_region": null, "date": "2024-10-15", "venue": "ArtificialAnalysis.ai", "url": "https://artificialanalysis.ai/providers", "summary": "Independent benchmark across providers. MI300X providers show wider variance.", "candidate_bill": "Bill_5", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "B5 third-party \u2014 variance documented", "_appeared_in_sweeps": [ "sweep_1304" ] }, { "paper_id": "h2o-kv-cache-2023", "title": "H2O: Heavy-Hitter Oracle for Efficient Generative Inference", "authors": [ "Zhenyu Zhang", "Ying Sheng", "Tianyi Zhou", "et al." ], "affiliations": [], "country_region": null, "date": "2023-12-15", "venue": "NeurIPS 2023", "url": "https://arxiv.org/abs/2306.14048", "summary": "Aggressive KV cache eviction. ~20% capability drop on long contexts.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "B6 \u2014 KV cache compression has fidelity cost", "_appeared_in_sweeps": [ "sweep_1306" ] }, { "paper_id": "huawei-ascend-910c-2024", "title": "Huawei Ascend 910C launch", "authors": [ "Huawei" ], "affiliations": [], "country_region": null, "date": "2024-09-15", "venue": "Huawei Connect 2024", "url": "https://www.huawei.com/en/news", "summary": "China-domestic alternative to H100. Limited international availability.", "candidate_bill": "Bill_10", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "M4 \u2014 geo-restricted", "_appeared_in_sweeps": [ "sweep_1304" ] }, { "paper_id": "huawei-ascend-cluster-2024", "title": "Huawei Atlas Ascend 910C cluster announcement", "authors": [ "Huawei" ], "affiliations": [], "country_region": null, "date": "2024-09-15", "venue": "Huawei Connect 2024", "url": "https://www.huawei.com", "summary": "Ascend 910C in datacenter cluster. China-only market.", "candidate_bill": "Bill_11", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "Coverage limited; B11 fails outside ecosystem", "_appeared_in_sweeps": [ "sweep_1303" ] }, { "paper_id": "huawei-ascend-r1-2025-claim", "title": "Huawei Ascend 910C running DeepSeek R1", "authors": [ "Huawei" ], "affiliations": [], "country_region": null, "date": "2025-02-15", "venue": "Huawei press / SCMP", "url": "https://www.huawei.com", "summary": "Huawei claims Ascend 910C runs DeepSeek R1 at competitive throughput.", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.7, "watchlist_tier": null, "notes": "M1; no independent verification", "_appeared_in_sweeps": [ "sweep_1304" ] }, { "paper_id": "huggingface-quanto-2024", "title": "HuggingFace Quanto FP8/INT8/INT4 toolkit", "authors": [ "HuggingFace" ], "affiliations": [], "country_region": null, "date": "2024-04-15", "venue": "HF blog", "url": "https://huggingface.co/blog/quanto-introduction", "summary": "Quanto Python toolkit. Fidelity claims limited to standard benchmarks.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.75, "watchlist_tier": null, "notes": "M1 quasi-vendor; capability eval narrow", "_appeared_in_sweeps": [ "sweep_1305" ] }, { "paper_id": "kivi-kv-cache-2024", "title": "KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache", "authors": [ "Zirui Liu", "Jiayi Yuan", "Hongye Jin", "et al." ], "affiliations": [], "country_region": null, "date": "2024-02-15", "venue": "arXiv 2402.02750", "url": "https://arxiv.org/abs/2402.02750", "summary": "2-bit KV cache. 2.6x throughput. Fidelity claims.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.85, "watchlist_tier": null, "notes": "B6 partial", "_appeared_in_sweeps": [ "sweep_1305" ] }, { "paper_id": "ktransformers-2024-tsinghua", "title": "KTransformers: heterogeneous CPU+GPU inference", "authors": [ "Tsinghua MADSys lab" ], "affiliations": [], "country_region": null, "date": "2024-08-01", "venue": "KTransformers GitHub", "url": "https://github.com/kvcache-ai/ktransformers", "summary": "Heterogeneous offload for MoE models. Claims DeepSeek-V2 on 24GB consumer GPU.", "candidate_bill": "Bill_4", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.7, "watchlist_tier": null, "notes": "Consumer-grade fidelity; streaming-vs-batch behavior under contention not measured", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "kuleshov-q4-degradation-2024", "title": "Quantization degradation on reasoning tasks", "authors": [ "Kuleshov et al. (Cornell)" ], "affiliations": [], "country_region": null, "date": "2024-09-15", "venue": "arXiv preprint", "url": "https://arxiv.org/abs/2410.09083", "summary": "INT4 quant shows ~10% drop on reasoning tasks vs FP16.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "B8 \u2014 strong evidence quant degrades real capability", "_appeared_in_sweeps": [ "sweep_1305" ] }, { "paper_id": "kv-cache-deepspeed-fastgen-2024", "title": "DeepSpeed-FastGen dynamic SplitFuse", "authors": [ "Microsoft DeepSpeed" ], "affiliations": [], "country_region": null, "date": "2023-11-15", "venue": "Microsoft Research blog", "url": "https://github.com/microsoft/DeepSpeed", "summary": "Token-level scheduling with KV cache reuse.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.8, "watchlist_tier": null, "notes": "Vendor implementation", "_appeared_in_sweeps": [ "sweep_1306" ] }, { "paper_id": "kv-cache-quant-kvquant-2024", "title": "KVQuant: Towards 10 Million Context Length", "authors": [ "Coleman Hooper", "Sehoon Kim", "Hiva Mohammadzadeh", "et al." ], "affiliations": [], "country_region": null, "date": "2024-02-15", "venue": "arXiv 2401.18079", "url": "https://arxiv.org/abs/2401.18079", "summary": "Per-token + per-channel KV cache quant. 4-bit KV with minimal degradation.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.85, "watchlist_tier": null, "notes": "Independent", "_appeared_in_sweeps": [ "sweep_1305" ] }, { "paper_id": "kv-cache-storage-cost-2024", "title": "KV Cache as a percentage of inference cost", "authors": [ "SemiAnalysis" ], "affiliations": [], "country_region": null, "date": "2024-09-15", "venue": "SemiAnalysis", "url": "https://www.semianalysis.com", "summary": "Estimates KV cache memory dominates inference cost at long context.", "candidate_bill": "Bill_2", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "B2 \u2014 cost transparency around KV cache", "_appeared_in_sweeps": [ "sweep_1306" ] }, { "paper_id": "kwon-2023-vllm-pagedattention-sosp", "title": "Efficient Memory Management for Large Language Model Serving with PagedAttention", "authors": [ "Woosuk Kwon", "Zhuohan Li", "Siyuan Zhuang", "et al." ], "affiliations": [], "country_region": null, "date": "2023-10-15", "venue": "SOSP 2023", "url": "https://arxiv.org/abs/2309.06180", "summary": "Original PagedAttention paper. Demonstrates 2-4x throughput improvements at matched latency.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.95, "watchlist_tier": null, "notes": "Peer-reviewed, widely reproduced. Baseline reference.", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "lightmatter-2025-funding", "title": "Lightmatter Series D funding for Passage 3D", "authors": [ "Lightmatter Inc." ], "affiliations": [], "country_region": null, "date": "2024-10-15", "venue": "Lightmatter press release", "url": "https://lightmatter.co/news/lightmatter-passage-launch/", "summary": "Continued funding; no commercial LLM inference benchmark.", "candidate_bill": "Bill_10", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "Research-preview", "_appeared_in_sweeps": [ "sweep_1302" ] }, { "paper_id": "lightmatter-envise-2024", "title": "Lightmatter Envise: photonic inference chip", "authors": [ "Lightmatter Inc." ], "affiliations": [], "country_region": null, "date": "2023-12-15", "venue": "Lightmatter announcements", "url": "https://lightmatter.co/products/envise/", "summary": "Photonic compute claims; no public LLM benchmark.", "candidate_bill": "Bill_10", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "Research-preview gap", "_appeared_in_sweeps": [ "sweep_1302" ] }, { "paper_id": "lightmatter-passage-2024-natelectronics", "title": "Lightmatter Passage optical interconnect", "authors": [ "Lightmatter Inc." ], "affiliations": [], "country_region": null, "date": "2024-09-26", "venue": "Nature Electronics + Lightmatter blog", "url": "https://lightmatter.co/products/passage/", "summary": "Photonic interposer for high-bandwidth chip-to-chip. No commercial inference benchmark yet.", "candidate_bill": "Bill_10", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.85, "watchlist_tier": null, "notes": "M4 commercialization-vs-research; no available product", "_appeared_in_sweeps": [ "sweep_1302" ] }, { "paper_id": "llama-cpp-quant-tests-2024", "title": "llama.cpp K-quant accuracy ablation (community)", "authors": [ "IK_llama and community" ], "affiliations": [], "country_region": null, "date": "2024-07-15", "venue": "llama.cpp wiki", "url": "https://github.com/ggerganov/llama.cpp/wiki/Tensor-Encoding-Schemes", "summary": "Q2_K through Q8_0 quantization with PPL deltas. Q4_K_M: ~3% PPL increase from FP16.", "candidate_bill": "Bill_3", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.75, "watchlist_tier": null, "notes": "PPL-only; not capability/reasoning eval", "_appeared_in_sweeps": [ "sweep_1305" ] }, { "paper_id": "llamacpp-2024-q4km-readme", "title": "llama.cpp: GGUF Q4_K_M quantization characterization", "authors": [ "Georgi Gerganov", "llama.cpp contributors" ], "affiliations": [], "country_region": null, "date": "2024-08-15", "venue": "llama.cpp GitHub", "url": "https://github.com/ggerganov/llama.cpp/blob/master/examples/quantize/README.md", "summary": "Documents perplexity deltas for K-quant formats vs FP16 across Llama 2/3.", "candidate_bill": "Bill_3", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.75, "watchlist_tier": null, "notes": "Perplexity != downstream capability; many follow-up reports show large divergence on reasoning", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "llamacpp-vs-mlx-vs-ollama-2024", "title": "Local inference comparison: llama.cpp vs MLX vs Ollama", "authors": [ "Community benchmarks" ], "affiliations": [], "country_region": null, "date": "2024-09-15", "venue": "Hacker News / r/LocalLLaMA", "url": "https://news.ycombinator.com", "summary": "On M3 Max: MLX 1.3x faster than llama.cpp for 7B. Ollama wraps llama.cpp.", "candidate_bill": "Bill_7", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.75, "watchlist_tier": null, "notes": "B7 \u2014 open-stack variance", "_appeared_in_sweeps": [ "sweep_1307" ] }, { "paper_id": "llamacpp-vs-vllm-cpu-2024", "title": "llama.cpp CPU vs vLLM GPU at 7B", "authors": [ "Community" ], "affiliations": [], "country_region": null, "date": "2024-08-15", "venue": "GitHub discussions", "url": "https://github.com/ggerganov/llama.cpp", "summary": "llama.cpp on Threadripper competitive with vLLM on consumer GPU at 7B.", "candidate_bill": "Bill_7", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.7, "watchlist_tier": null, "notes": "B7", "_appeared_in_sweeps": [ "sweep_1307" ] }, { "paper_id": "llm-compressor-vllm-fp8-2024", "title": "llm-compressor library (vLLM team)", "authors": [ "vLLM team / Neural Magic" ], "affiliations": [], "country_region": null, "date": "2024-10-15", "venue": "GitHub", "url": "https://github.com/vllm-project/llm-compressor", "summary": "FP8/INT4/INT8 quant library for vLLM. Recipe-based.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.75, "watchlist_tier": null, "notes": "Vendor toolkit", "_appeared_in_sweeps": [ "sweep_1305" ] }, { "paper_id": "llm-int8-2022-dettmers", "title": "LLM.int8(): 8-bit Matrix Multiplication", "authors": [ "Tim Dettmers", "Mike Lewis", "Younes Belkada", "Luke Zettlemoyer" ], "affiliations": [], "country_region": null, "date": "2022-08-15", "venue": "NeurIPS 2022", "url": "https://arxiv.org/abs/2208.07339", "summary": "Foundational LLM int8 quant. Mixed-precision for outliers.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.95, "watchlist_tier": null, "notes": "Peer-reviewed baseline", "_appeared_in_sweeps": [ "sweep_1305" ] }, { "paper_id": "lmcache-2024-sglang-vllm", "title": "LMCache: Cross-instance KV caching", "authors": [ "LMCache team" ], "affiliations": [], "country_region": null, "date": "2024-06-15", "venue": "arXiv + LMCache GitHub", "url": "https://github.com/LMCache/LMCache", "summary": "Persistent KV cache across vLLM/SGLang instances.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.8, "watchlist_tier": null, "notes": "Mechanism plausible; large-scale prod data not yet public", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "lmdeploy-2024-internlm-rls", "title": "LMDeploy with TurboMind backend", "authors": [ "InternLM team" ], "affiliations": [], "country_region": null, "date": "2024-08-01", "venue": "LMDeploy GitHub", "url": "https://github.com/InternLM/lmdeploy", "summary": "Claims faster than vLLM on InternLM models. Cross-vendor at matched workload not reported.", "candidate_bill": "Bill_5", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.7, "watchlist_tier": null, "notes": "Self-reported, vendor-favorable model selection", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "lmstudio-2024-rls-readme", "title": "LM Studio runtime engines", "authors": [ "LM Studio Inc." ], "affiliations": [], "country_region": null, "date": "2024-10-01", "venue": "LM Studio docs", "url": "https://lmstudio.ai", "summary": "GUI wrapper over llama.cpp/MLX/Ollama. No published independent perf measurements.", "candidate_bill": "Bill_10", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "Marketing-grade benchmark transparency only", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "lookahead-decoding-2024-mit", "title": "Lookahead Decoding for Parallel LLM Generation", "authors": [ "Yichao Fu", "Peter Bailis", "Ion Stoica", "Hao Zhang" ], "affiliations": [], "country_region": null, "date": "2024-02-15", "venue": "ICML 2024", "url": "https://arxiv.org/abs/2402.02057", "summary": "No draft model needed. Parallel n-gram speculative.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.85, "watchlist_tier": null, "notes": "Independent", "_appeared_in_sweeps": [ "sweep_1306" ] }, { "paper_id": "lookahead-spec-decode-mlx-2024", "title": "MLX speculative decoding integration", "authors": [ "Apple ML" ], "affiliations": [], "country_region": null, "date": "2024-09-15", "venue": "MLX examples", "url": "https://github.com/ml-explore/mlx-examples", "summary": "MLX supports speculative decoding. Apple Silicon-only.", "candidate_bill": "Bill_11", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.75, "watchlist_tier": null, "notes": "B11 \u2014 platform-limited", "_appeared_in_sweeps": [ "sweep_1306" ] }, { "paper_id": "marlin-vllm-int4-2024", "title": "Marlin: Mixed Precision Inference Kernels", "authors": [ "IST Austria / Alistarh group" ], "affiliations": [], "country_region": null, "date": "2024-04-15", "venue": "arXiv 2408.11743", "url": "https://arxiv.org/abs/2408.11743", "summary": "INT4 W4A16 kernels for A100/H100. Throughput claims.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "Academic", "_appeared_in_sweeps": [ "sweep_1305" ] }, { "paper_id": "medusa-2024-mlsys", "title": "Medusa: Simple LLM Inference Acceleration Framework", "authors": [ "Tianle Cai", "Yuhong Li", "Zhengyang Geng", "et al." ], "affiliations": [], "country_region": null, "date": "2024-01-15", "venue": "arXiv 2401.10774", "url": "https://arxiv.org/abs/2401.10774", "summary": "Multi-head speculative draft. Self-draft. 2.3x speedup.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.9, "watchlist_tier": null, "notes": "Independent; Vicuna eval", "_appeared_in_sweeps": [ "sweep_1306" ] }, { "paper_id": "meta-mtia-v2-2024", "title": "Meta MTIA v2 inference chip", "authors": [ "Meta" ], "affiliations": [], "country_region": null, "date": "2024-04-10", "venue": "Meta engineering blog", "url": "https://ai.meta.com/blog/next-generation-meta-training-inference-accelerator-AI-MTIA/", "summary": "Internal-only inference chip for Meta workloads.", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "M5 platform-lock; M1 self-eval", "_appeared_in_sweeps": [ "sweep_1303" ] }, { "paper_id": "metr-frontier-2025", "title": "METR frontier model autonomy evaluations", "authors": [ "METR" ], "affiliations": [], "country_region": null, "date": "2025-03-15", "venue": "METR", "url": "https://metr.org", "summary": "Quantization-impact on agentic task completion.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "B8", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "metr-quant-eval-2024", "title": "METR: Evaluating quantized model capabilities", "authors": [ "METR (Megan Kinniment et al.)" ], "affiliations": [], "country_region": null, "date": "2024-08-15", "venue": "METR blog", "url": "https://metr.org", "summary": "Quantization impact on agentic capability. Reveals downstream degradation invisible to MMLU.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "B8 \u2014 fidelity audit shows quant degrades agentic capability", "_appeared_in_sweeps": [ "sweep_1305" ] }, { "paper_id": "mi300x-vs-h100-semianalysis-2024", "title": "SemiAnalysis: MI300X vs H100 real-world", "authors": [ "SemiAnalysis (Dylan Patel)" ], "affiliations": [], "country_region": null, "date": "2024-12-15", "venue": "SemiAnalysis Substack", "url": "https://www.semianalysis.com/p/mi300x-vs-h100-vs-h200-benchmark", "summary": "Independent extensive benchmark. MI300X advertised but ROCm gaps in real workload.", "candidate_bill": "Bill_7", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.95, "watchlist_tier": null, "notes": "Strong third-party analysis; partially closes B7 with caveats", "_appeared_in_sweeps": [ "sweep_1304" ] }, { "paper_id": "microsoft-maia-100-2023-ignite", "title": "Microsoft Maia 100 launch", "authors": [ "Microsoft Azure" ], "affiliations": [], "country_region": null, "date": "2023-11-15", "venue": "Microsoft Ignite 2023", "url": "https://news.microsoft.com/source/features/ai/in-house-chips-silicon-to-service-to-meet-ai-demand/", "summary": "First Microsoft AI accelerator. Internal use for OpenAI workloads.", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "M1 vendor-only; M5 platform-lock", "_appeared_in_sweeps": [ "sweep_1303" ] }, { "paper_id": "microsoft-maia-200-2025-rumor", "title": "Microsoft Maia 200 (Athena) reports", "authors": [ "industry analysts" ], "affiliations": [], "country_region": null, "date": "2025-02-15", "venue": "The Information + SemiAnalysis", "url": "https://www.theinformation.com", "summary": "Reports of Maia 200 in development. No public benchmark.", "candidate_bill": "Bill_10", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.7, "watchlist_tier": null, "notes": "Research-preview", "_appeared_in_sweeps": [ "sweep_1303" ] }, { "paper_id": "minference-2024-microsoft", "title": "MInference: Dynamic Sparse Attention", "authors": [ "Huiqiang Jiang", "Yucheng Li", "Chengruidong Zhang", "et al." ], "affiliations": [], "country_region": null, "date": "2024-07-15", "venue": "NeurIPS 2024", "url": "https://arxiv.org/abs/2407.02490", "summary": "Dynamic sparse attention for long context. ~10x speedup.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.85, "watchlist_tier": null, "notes": "B6", "_appeared_in_sweeps": [ "sweep_1306" ] }, { "paper_id": "minicache-kv-2024", "title": "MiniCache: KV Cache Compression in Depth", "authors": [ "Akide Liu", "Jing Liu", "Zizheng Pan", "et al." ], "affiliations": [], "country_region": null, "date": "2024-05-15", "venue": "arXiv 2405.14366", "url": "https://arxiv.org/abs/2405.14366", "summary": "Layer-merging KV cache. 5x KV cache reduction.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.85, "watchlist_tier": null, "notes": "B6", "_appeared_in_sweeps": [ "sweep_1306" ] }, { "paper_id": "mlc-mlx-quant-2024", "title": "MLX 4-bit quant on Apple Silicon", "authors": [ "Apple ML" ], "affiliations": [], "country_region": null, "date": "2024-04-15", "venue": "MLX docs", "url": "https://github.com/ml-explore/mlx-examples", "summary": "4-bit quant for on-device Apple Silicon. Limited fidelity audit.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.7, "watchlist_tier": null, "notes": "B8", "_appeared_in_sweeps": [ "sweep_1305" ] }, { "paper_id": "mlperf-inf-v4.1-tpu-2024", "title": "MLPerf Inference v4.1 TPU submissions", "authors": [ "MLCommons" ], "affiliations": [], "country_region": null, "date": "2024-09-04", "venue": "MLPerf", "url": "https://mlcommons.org/benchmarks/inference-datacenter/", "summary": "Google submitted TPU v5e results. Closed division.", "candidate_bill": "Bill_9", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.7, "watchlist_tier": null, "notes": "Vendor-submitter bias", "_appeared_in_sweeps": [ "sweep_1303" ] }, { "paper_id": "mlperf-inference-v4-1-submitters-2024", "title": "MLPerf v4.1 submitter analysis", "authors": [ "MLCommons" ], "affiliations": [], "country_region": null, "date": "2024-09-04", "venue": "MLPerf", "url": "https://mlcommons.org", "summary": "Most submissions by NVIDIA, AMD, Intel-collaborators. Closed vendors absent.", "candidate_bill": "Bill_9", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.9, "watchlist_tier": null, "notes": "B9 \u2014 coverage skewed", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "mlperf-inference-v5-2025", "title": "MLPerf Inference v5.0 results", "authors": [ "MLCommons" ], "affiliations": [], "country_region": null, "date": "2025-04-02", "venue": "MLPerf", "url": "https://mlcommons.org/benchmarks/inference-datacenter/", "summary": "v5.0 includes Llama 3.1 405B. NVIDIA Blackwell dominant.", "candidate_bill": "Bill_9", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.9, "watchlist_tier": null, "notes": "B9 \u2014 vendor self-eval dominance", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "mlperf-llama2-70b-2024-vendor-coverage", "title": "MLPerf Llama 2 70B vendor coverage gap", "authors": [ "MLCommons" ], "affiliations": [], "country_region": null, "date": "2024-09-04", "venue": "MLPerf", "url": "https://mlcommons.org", "summary": "Many vendors absent from Llama 2 70B server scenario.", "candidate_bill": "Bill_9", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.9, "watchlist_tier": null, "notes": "B9", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "mlperf-power-2024", "title": "MLPerf Inference Power category", "authors": [ "MLCommons" ], "affiliations": [], "country_region": null, "date": "2024-09-04", "venue": "MLPerf", "url": "https://mlcommons.org", "summary": "Power category limited submitters; standardization difficult.", "candidate_bill": "Bill_13", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "B13 audit gap", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "mlx-2024-apple-paper-arxiv", "title": "MLX: A unified array framework for Apple silicon", "authors": [ "Apple ML Research" ], "affiliations": [], "country_region": null, "date": "2024-03-12", "venue": "Apple ML Research blog + GitHub", "url": "https://github.com/ml-explore/mlx", "summary": "Unified memory framework, lazy compute, M2/M3 optimization. No cross-vendor variance metrics published.", "candidate_bill": "Bill_11", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "Apple-silicon-only; not directly comparable to CUDA stack at matched compute", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "mlx-vs-llamacpp-q4_k_m-2024", "title": "MLX Q4 vs llama.cpp Q4_K_M on M3 Max", "authors": [ "Community" ], "affiliations": [], "country_region": null, "date": "2024-09-15", "venue": "GitHub discussions", "url": "https://github.com/ml-explore/mlx", "summary": "MLX 1.2-1.5x faster than llama.cpp on same hardware.", "candidate_bill": "Bill_5", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.75, "watchlist_tier": null, "notes": "B5", "_appeared_in_sweeps": [ "sweep_1307" ] }, { "paper_id": "mlx-vs-vllm-7b-2024", "title": "Local MLX vs cloud vLLM 7B comparison", "authors": [ "Community" ], "affiliations": [], "country_region": null, "date": "2024-08-15", "venue": "Hacker News", "url": "https://news.ycombinator.com", "summary": "MLX on M3 Max comparable to cloud H100 for batch=1 7B.", "candidate_bill": "Bill_7", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.7, "watchlist_tier": null, "notes": "B7 \u2014 surprising; reproducible", "_appeared_in_sweeps": [ "sweep_1307" ] }, { "paper_id": "model-extraction-attack-2024", "title": "Model extraction attacks via inference APIs", "authors": [ "Various security researchers" ], "affiliations": [], "country_region": null, "date": "2024-07-15", "venue": "arXiv 2407.06146", "url": "https://arxiv.org/abs/2407.06146", "summary": "Model extraction via inference API logit access.", "candidate_bill": "Bill_13", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.85, "watchlist_tier": null, "notes": "B13 \u2014 safety/extraction angle", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "modular-vs-vllm-vs-trt-2024", "title": "Modular MAX vs vLLM vs TRT-LLM", "authors": [ "Modular Inc." ], "affiliations": [], "country_region": null, "date": "2024-09-15", "venue": "Modular blog", "url": "https://www.modular.com", "summary": "Self-promotional; cross-framework variance documented.", "candidate_bill": "Bill_5", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.7, "watchlist_tier": null, "notes": "M1 self-interested but data documented", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "moonshot-deepseek-h800-2024", "title": "Moonshot/Mooncake on H800 (export-controlled)", "authors": [ "Moonshot AI" ], "affiliations": [], "country_region": null, "date": "2024-06-24", "venue": "arXiv", "url": "https://arxiv.org/abs/2407.00079", "summary": "H800 fleet for Kimi. Cross-hardware portability not benchmarked.", "candidate_bill": "Bill_11", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.75, "watchlist_tier": null, "notes": "Single-architecture", "_appeared_in_sweeps": [ "sweep_1304" ] }, { "paper_id": "moreh-amd-2024-llama-3", "title": "Moreh AMD MI300X Llama 3 70B benchmark", "authors": [ "Moreh AI" ], "affiliations": [], "country_region": null, "date": "2024-07-15", "venue": "Moreh blog", "url": "https://moreh.io", "summary": "Third-party MI300X inference measurements.", "candidate_bill": "Bill_7", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.75, "watchlist_tier": null, "notes": "Limited third-party data", "_appeared_in_sweeps": [ "sweep_1304" ] }, { "paper_id": "moreh-mi300-2024-benchmark", "title": "Moreh + AMD Llama 3 405B run", "authors": [ "Moreh + AMD" ], "affiliations": [], "country_region": null, "date": "2024-08-15", "venue": "Moreh blog", "url": "https://moreh.io", "summary": "Joint MI300X 405B run. Vendor-friendly.", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.7, "watchlist_tier": null, "notes": "M1", "_appeared_in_sweeps": [ "sweep_1304" ] }, { "paper_id": "negative-results-llm-bench-2024", "title": "Negative-results LLM benchmark hardware analysis", "authors": [ "Various academic" ], "affiliations": [], "country_region": null, "date": "2024-10-15", "venue": "Workshop on negative results", "url": "https://arxiv.org", "summary": "Highlights variability of LLM benchmark results across hardware.", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.75, "watchlist_tier": null, "notes": "B1", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "neuralmagic-fp8-llama-2024", "title": "Neural Magic: FP8 W8A8 Llama 3 fidelity", "authors": [ "Neural Magic / Red Hat" ], "affiliations": [], "country_region": null, "date": "2024-07-15", "venue": "Neural Magic blog", "url": "https://neuralmagic.com", "summary": "FP8 quant of Llama 3.1 70B and 405B. Claims <1% drop on standard benchmarks.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "M1 vendor; benchmark-narrow", "_appeared_in_sweeps": [ "sweep_1305" ] }, { "paper_id": "neuron-sdk-2024-aws-rls", "title": "AWS Neuron SDK for Trainium/Inferentia", "authors": [ "AWS Annapurna" ], "affiliations": [], "country_region": null, "date": "2024-09-15", "venue": "AWS Neuron docs", "url": "https://awsdocs-neuron.readthedocs-hosted.com", "summary": "AWS-specific compiler/runtime. Llama 3 70B support. No cross-cloud portability.", "candidate_bill": "Bill_11", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "Universal-platform fails \u2014 AWS-lock", "_appeared_in_sweeps": [ "sweep_1303" ] }, { "paper_id": "nvidia-b200-shipping-2024-delays", "title": "Blackwell B200 shipping delays reports", "authors": [ "The Information / Reuters" ], "affiliations": [], "country_region": null, "date": "2024-08-15", "venue": "The Information", "url": "https://www.theinformation.com", "summary": "Reports of 3-month Blackwell shipping delays due to design flaw.", "candidate_bill": "Bill_10", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "M4 commercialization-vs-research-preview", "_appeared_in_sweeps": [ "sweep_1304" ] }, { "paper_id": "nvidia-blackwell-b100-2024-gtc", "title": "NVIDIA Blackwell B100/B200 GTC 2024 launch", "authors": [ "NVIDIA" ], "affiliations": [], "country_region": null, "date": "2024-03-18", "venue": "NVIDIA GTC 2024", "url": "https://nvidianews.nvidia.com/news/nvidia-blackwell-platform-arrives-to-power-a-new-era-of-computing", "summary": "Blackwell B100 (192GB) / B200 (192GB) with FP4 native. Claims 30x training perf, 25x inference.", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "M1 vendor-supplied; first-deliverable Q4 2024", "_appeared_in_sweeps": [ "sweep_1304" ] }, { "paper_id": "nvidia-deepseek-r1-h200-2025", "title": "NVIDIA NIM DeepSeek R1 671B on H200", "authors": [ "NVIDIA" ], "affiliations": [], "country_region": null, "date": "2025-01-31", "venue": "NVIDIA blog", "url": "https://blogs.nvidia.com/blog/deepseek-r1-nim-microservice/", "summary": "Full 671B DeepSeek R1 on 8x H200. Vendor benchmark.", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "M1 vendor self-eval", "_appeared_in_sweeps": [ "sweep_1304" ] }, { "paper_id": "nvidia-fp8-fidelity-claim-blog", "title": "NVIDIA FP8 fidelity case studies", "authors": [ "NVIDIA" ], "affiliations": [], "country_region": null, "date": "2024-06-15", "venue": "NVIDIA dev blog", "url": "https://developer.nvidia.com", "summary": "Aggregated FP8 fidelity claims. MMLU-centric.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "M1; downstream reasoning fidelity not measured", "_appeared_in_sweeps": [ "sweep_1304" ] }, { "paper_id": "nvidia-gb200-nvl72-2024", "title": "GB200 NVL72 rack-scale launch", "authors": [ "NVIDIA" ], "affiliations": [], "country_region": null, "date": "2024-03-18", "venue": "NVIDIA GTC 2024", "url": "https://www.nvidia.com/en-us/data-center/gb200-nvl72/", "summary": "72-GPU NVL72 rack with NVLink5. Claims 30x perf for trillion-param inference.", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "M1; deliveries Q1 2025", "_appeared_in_sweeps": [ "sweep_1304" ] }, { "paper_id": "nvidia-h200-2023-launch", "title": "NVIDIA H200 launch announcement", "authors": [ "NVIDIA" ], "affiliations": [], "country_region": null, "date": "2023-11-13", "venue": "NVIDIA press release", "url": "https://nvidianews.nvidia.com/news/nvidia-supercharges-hopper-the-worlds-leading-ai-computing-platform", "summary": "H200 with 141GB HBM3e. Claims 2x Llama 2 70B inference perf vs H100.", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "M1 vendor-supplied", "_appeared_in_sweeps": [ "sweep_1304" ] }, { "paper_id": "nvidia-h200-mlperf-2024", "title": "H200 MLPerf Inference 4.1 results", "authors": [ "NVIDIA" ], "affiliations": [], "country_region": null, "date": "2024-09-04", "venue": "MLPerf", "url": "https://mlcommons.org/benchmarks/inference-datacenter/", "summary": "H200 results. NVIDIA-only submitter for some categories.", "candidate_bill": "Bill_9", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "Vendor-submitter", "_appeared_in_sweeps": [ "sweep_1304" ] }, { "paper_id": "nvidia-power-h100-2023", "title": "NVIDIA H100 power and thermals", "authors": [ "NVIDIA datasheet" ], "affiliations": [], "country_region": null, "date": "2023-03-15", "venue": "NVIDIA datasheet", "url": "https://www.nvidia.com/en-us/data-center/h100/", "summary": "700W TDP H100 SXM. Power-per-token characterization not standardized.", "candidate_bill": "Bill_13", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.75, "watchlist_tier": null, "notes": "B13 power efficiency audit gap", "_appeared_in_sweeps": [ "sweep_1304" ] }, { "paper_id": "nvidia-rubin-2025-roadmap", "title": "NVIDIA Rubin roadmap reveal", "authors": [ "NVIDIA" ], "affiliations": [], "country_region": null, "date": "2024-06-02", "venue": "Computex 2024", "url": "https://www.nvidia.com", "summary": "Rubin GPU + Vera CPU roadmap. 2026 delivery.", "candidate_bill": "Bill_10", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.85, "watchlist_tier": null, "notes": "Pre-product", "_appeared_in_sweeps": [ "sweep_1304" ] }, { "paper_id": "nvidia-tensorrt-llm-fp4-2024", "title": "TensorRT-LLM FP4 on Blackwell", "authors": [ "NVIDIA" ], "affiliations": [], "country_region": null, "date": "2024-12-15", "venue": "NVIDIA blog", "url": "https://developer.nvidia.com/blog/nvidia-blackwell-platform-accelerates-llm-inference-with-fp4/", "summary": "FP4 quantization on Blackwell. Claims 2x speedup vs FP8. Fidelity claims for MMLU only.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "M1 vendor; B8 narrow eval", "_appeared_in_sweeps": [ "sweep_1304" ] }, { "paper_id": "ollama-2024-rls-notes", "title": "Ollama release notes 0.3+", "authors": [ "Ollama team" ], "affiliations": [], "country_region": null, "date": "2024-09-20", "venue": "Ollama GitHub releases", "url": "https://github.com/ollama/ollama/releases", "summary": "Wraps llama.cpp; no independent throughput benchmarks vs vLLM/SGLang at matched precision.", "candidate_bill": "Bill_10", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "Distribution layer; inherits llama.cpp characteristics. Commercial-availability vs research-preview overlap.", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "open-source-int4-llama3-eval-2024", "title": "Open eval: Llama 3 INT4 across HumanEval/MATH/GSM8k", "authors": [ "Eleuther + LM Eval Harness community" ], "affiliations": [], "country_region": null, "date": "2024-08-15", "venue": "GitHub + arxiv", "url": "https://github.com/EleutherAI/lm-evaluation-harness", "summary": "Llama 3 8B INT4 shows -5 to -8% on MATH and GSM8k vs FP16.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "B8 \u2014 independent third-party reveals reasoning gap", "_appeared_in_sweeps": [ "sweep_1305" ] }, { "paper_id": "open-vs-closed-mlperf-coverage-2024", "title": "MLPerf Inference: open vs closed vendor coverage", "authors": [ "MLCommons" ], "affiliations": [], "country_region": null, "date": "2024-09-04", "venue": "MLPerf", "url": "https://mlcommons.org", "summary": "NVIDIA dominant in submitter count; AMD/Intel small participation; Groq/Cerebras/SambaNova absent.", "candidate_bill": "Bill_9", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.9, "watchlist_tier": null, "notes": "B9 \u2014 vendor coverage biased", "_appeared_in_sweeps": [ "sweep_1307" ] }, { "paper_id": "openai-api-cost-2024", "title": "OpenAI API pricing transparency", "authors": [ "OpenAI" ], "affiliations": [], "country_region": null, "date": "2024-09-15", "venue": "OpenAI pricing", "url": "https://openai.com/api/pricing/", "summary": "Token pricing; closed shop infra costs.", "candidate_bill": "Bill_2", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "B2", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "openai-broadcom-2025-rumor", "title": "OpenAI custom chip taping out Q2 2026", "authors": [ "FT + Reuters reports" ], "affiliations": [], "country_region": null, "date": "2025-02-15", "venue": "FT", "url": "https://www.ft.com/content/8c1ec0a5", "summary": "Reports of OpenAI tape-out via Broadcom + TSMC. No specs.", "candidate_bill": "Bill_10", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.7, "watchlist_tier": null, "notes": "Pre-silicon", "_appeared_in_sweeps": [ "sweep_1303" ] }, { "paper_id": "openai-custom-silicon-2024-reuters", "title": "OpenAI partnering with Broadcom for custom inference chip", "authors": [ "Reuters / industry" ], "affiliations": [], "country_region": null, "date": "2024-10-29", "venue": "Reuters", "url": "https://www.reuters.com/technology/artificial-intelligence/openai-builds-first-chip-with-broadcom-tsmc-scales-back-foundry-ambition-2024-10-29/", "summary": "Broadcom + TSMC partnership for custom OpenAI inference chip. Targeted 2026.", "candidate_bill": "Bill_10", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.8, "watchlist_tier": null, "notes": "M4 commercialization gap; pre-silicon", "_appeared_in_sweeps": [ "sweep_1303" ] }, { "paper_id": "openai-stargate-2025-jan", "title": "OpenAI Stargate compute commitment ($500B)", "authors": [ "OpenAI press / Reuters" ], "affiliations": [], "country_region": null, "date": "2025-01-21", "venue": "White House announcement", "url": "https://www.reuters.com/technology/artificial-intelligence/openai-oracle-softbank-launch-stargate-joint-venture-build-out-ai-infrastructure-2025-01-21/", "summary": "OpenAI+Oracle+SoftBank $500B compute commitment. Hardware mix unspecified.", "candidate_bill": "Bill_10", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.75, "watchlist_tier": null, "notes": "Procurement, not benchmark", "_appeared_in_sweeps": [ "sweep_1303" ] }, { "paper_id": "openrouter-cost-leaderboard-2024", "title": "OpenRouter LLM provider price comparison", "authors": [ "OpenRouter" ], "affiliations": [], "country_region": null, "date": "2024-12-15", "venue": "OpenRouter", "url": "https://openrouter.ai/rankings", "summary": "Live cost/throughput ranking. Same model varies 10x across providers.", "candidate_bill": "Bill_2", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.9, "watchlist_tier": null, "notes": "B2/B5 \u2014 third-party", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "openrouter-throughput-2025", "title": "OpenRouter throughput analytics", "authors": [ "OpenRouter" ], "affiliations": [], "country_region": null, "date": "2025-03-15", "venue": "OpenRouter", "url": "https://openrouter.ai/rankings", "summary": "Live throughput by provider. >10x variance same model.", "candidate_bill": "Bill_5", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.9, "watchlist_tier": null, "notes": "B5", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "outlier-aware-quant-2024", "title": "OmniQuant: Omnidirectionally Calibrated Quantization", "authors": [ "Wenqi Shao", "Mengzhao Chen", "Zhaoyang Zhang", "et al." ], "affiliations": [], "country_region": null, "date": "2024-04-15", "venue": "ICLR 2024", "url": "https://arxiv.org/abs/2308.13137", "summary": "Outlier-aware quant. Llama-2-70B W2A16 with reasonable fidelity.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "ICLR peer-reviewed", "_appeared_in_sweeps": [ "sweep_1305" ] }, { "paper_id": "paged-attention-validation-2024", "title": "PagedAttention correctness validation", "authors": [ "vLLM community" ], "affiliations": [], "country_region": null, "date": "2024-05-15", "venue": "vLLM GitHub issues", "url": "https://github.com/vllm-project/vllm", "summary": "Reports of minor numerical issues in PagedAttention vs FA2 reference. Mostly resolved.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.75, "watchlist_tier": null, "notes": "B6 \u2014 kernel fidelity issues historically present", "_appeared_in_sweeps": [ "sweep_1306" ] }, { "paper_id": "perplexity-tput-blog-2024", "title": "Perplexity inference stack characterization", "authors": [ "Perplexity AI engineering" ], "affiliations": [], "country_region": null, "date": "2024-08-15", "venue": "Perplexity blog", "url": "https://www.perplexity.ai/hub", "summary": "Mentions vLLM-derivative + custom kernels for production. No published variance data.", "candidate_bill": "Bill_2", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.7, "watchlist_tier": null, "notes": "Proprietary; not reproducible", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "qkv-projection-merging-2024", "title": "QKV projection fusing on H100", "authors": [ "Various" ], "affiliations": [], "country_region": null, "date": "2024-06-15", "venue": "Triton kernel community", "url": "https://github.com/openai/triton", "summary": "Kernel-level optimization for QKV. Numerical fidelity vs reference checked.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.8, "watchlist_tier": null, "notes": "Kernel work", "_appeared_in_sweeps": [ "sweep_1306" ] }, { "paper_id": "qlora-2023-dettmers", "title": "QLoRA: Efficient Finetuning of Quantized LLMs", "authors": [ "Tim Dettmers", "Artidoro Pagnoni", "Ari Holtzman", "Luke Zettlemoyer" ], "affiliations": [], "country_region": null, "date": "2023-12-15", "venue": "NeurIPS 2023", "url": "https://arxiv.org/abs/2305.14314", "summary": "NF4 4-bit + LoRA. Demonstrates fine-tuning works on 4-bit base.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.95, "watchlist_tier": null, "notes": "Peer-reviewed", "_appeared_in_sweeps": [ "sweep_1305" ] }, { "paper_id": "qserve-2024-mit-han", "title": "QServe: W4A8KV4 Quantization with INT4 KV cache", "authors": [ "Yujun Lin", "Haotian Tang", "Shang Yang", "et al." ], "affiliations": [], "country_region": null, "date": "2024-05-15", "venue": "arXiv 2405.04532", "url": "https://arxiv.org/abs/2405.04532", "summary": "Full INT4 quant including KV cache. 2.4x throughput vs FP16. Fidelity claims.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.85, "watchlist_tier": null, "notes": "Academic; B6/B8 both relevant", "_appeared_in_sweeps": [ "sweep_1305" ] }, { "paper_id": "qstar-quantize-2024", "title": "Quantizing LLMs for Reasoning: A Survey", "authors": [ "Various authors" ], "affiliations": [], "country_region": null, "date": "2024-10-15", "venue": "arXiv preprint", "url": "https://arxiv.org/abs/2410.00037", "summary": "Survey of quant reasoning impacts. Shows capability gaps invisible to PPL.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "B8 \u2014 capability vs PPL divergence", "_appeared_in_sweeps": [ "sweep_1305" ] }, { "paper_id": "quantization-impact-agentic-2024", "title": "Quantization impact on agentic tool-use capability", "authors": [ "METR + community" ], "affiliations": [], "country_region": null, "date": "2024-11-15", "venue": "METR / community evals", "url": "https://metr.org", "summary": "INT4 quantized models show step-tier degradation on agentic tasks, even when MMLU is preserved.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "Strong B8 evidence \u2014 capability fidelity gap", "_appeared_in_sweeps": [ "sweep_1305" ] }, { "paper_id": "quarot-2024-microsoft", "title": "QuaRot: Outlier-free 4-bit Inference", "authors": [ "Saleh Ashkboos", "Amirkeivan Mohtashami", "Maximilian Croci", "et al." ], "affiliations": [], "country_region": null, "date": "2024-04-15", "venue": "arXiv 2404.00456", "url": "https://arxiv.org/abs/2404.00456", "summary": "Hadamard rotation enables 4-bit weight + activation + KV cache.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "Microsoft academic", "_appeared_in_sweeps": [ "sweep_1305" ] }, { "paper_id": "rain-ai-2024-altman-funding", "title": "Rain Neuromorphics analog-AI chip", "authors": [ "Rain AI" ], "affiliations": [], "country_region": null, "date": "2023-08-15", "venue": "Rain.ai company materials", "url": "https://rain.ai", "summary": "Analog neuromorphic for inference. No public LLM benchmark.", "candidate_bill": "Bill_10", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "Research-preview only", "_appeared_in_sweeps": [ "sweep_1302" ] }, { "paper_id": "rain-ai-fundraising-2025", "title": "Rain.ai $150M Series B", "authors": [ "Rain.ai press" ], "affiliations": [], "country_region": null, "date": "2025-01-10", "venue": "Press", "url": "https://rain.ai", "summary": "Funding without delivered hardware benchmark.", "candidate_bill": "Bill_10", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.85, "watchlist_tier": null, "notes": "Pre-product", "_appeared_in_sweeps": [ "sweep_1302" ] }, { "paper_id": "rayllm-2024-anyscale", "title": "RayLLM (now Anyscale): vLLM at scale", "authors": [ "Anyscale" ], "affiliations": [], "country_region": null, "date": "2024-05-01", "venue": "Anyscale blog", "url": "https://docs.anyscale.com/llms/serving/intro/", "summary": "Production wrapper over vLLM. Cost-per-token transparency in commercial service only.", "candidate_bill": "Bill_2", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.7, "watchlist_tier": null, "notes": "Commercial wrapper; not independent benchmark", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "rest-spec-decode-2024", "title": "REST: Retrieval-Based Speculative Decoding", "authors": [ "Zhenyu He", "Zexuan Zhong", "Tianle Cai", "et al." ], "affiliations": [], "country_region": null, "date": "2024-03-15", "venue": "NAACL 2024", "url": "https://arxiv.org/abs/2311.08252", "summary": "Retrieval-augmented speculative drafts. Speedups for code domain.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.85, "watchlist_tier": null, "notes": "Peer-reviewed", "_appeared_in_sweeps": [ "sweep_1306" ] }, { "paper_id": "rocm-vllm-2024-amd-blog", "title": "ROCm 6 + vLLM AMD MI300X support", "authors": [ "AMD ROCm team" ], "affiliations": [], "country_region": null, "date": "2024-06-15", "venue": "AMD blog", "url": "https://www.amd.com/en/developer/resources/rocm-revision-history.html", "summary": "vLLM full support on MI300X. ROCm stack maturity claims.", "candidate_bill": "Bill_11", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "Software stack still less mature than CUDA", "_appeared_in_sweeps": [ "sweep_1304" ] }, { "paper_id": "rocm-vllm-mi300x-vs-cuda-2024", "title": "ROCm vLLM MI300X vs CUDA H100", "authors": [ "SemiAnalysis" ], "affiliations": [], "country_region": null, "date": "2024-12-15", "venue": "SemiAnalysis", "url": "https://www.semianalysis.com/p/mi300x-vs-h100-vs-h200-benchmark", "summary": "Real-world vLLM ROCm vs CUDA shows software gap impacting MI300X.", "candidate_bill": "Bill_11", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.9, "watchlist_tier": null, "notes": "B7/B11 \u2014 universal coverage fails across vendors", "_appeared_in_sweeps": [ "sweep_1307" ] }, { "paper_id": "sambanova-405b-2024", "title": "SambaNova Llama 3.1 405B service", "authors": [ "SambaNova" ], "affiliations": [], "country_region": null, "date": "2024-09-10", "venue": "SambaNova blog", "url": "https://sambanova.ai/blog/llama-3-1-405b", "summary": "Claims 132 tok/s on full Llama 3.1 405B.", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "Vendor-supplied; M1", "_appeared_in_sweeps": [ "sweep_1302" ] }, { "paper_id": "sambanova-coherent-2024", "title": "SambaNova Composition-of-Experts agent serving", "authors": [ "SambaNova" ], "affiliations": [], "country_region": null, "date": "2024-10-15", "venue": "SambaNova blog", "url": "https://sambanova.ai/blog/composition-of-experts", "summary": "Multi-model agent dispatch. Cross-vendor variance not measured.", "candidate_bill": "Bill_11", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.75, "watchlist_tier": null, "notes": "Vendor-platform-only", "_appeared_in_sweeps": [ "sweep_1302" ] }, { "paper_id": "sambanova-fast-api-2024", "title": "SambaNova Cloud Fast API launch", "authors": [ "SambaNova Systems" ], "affiliations": [], "country_region": null, "date": "2024-09-10", "venue": "SambaNova blog", "url": "https://sambanova.ai/press/sambanova-launches-the-worlds-fastest-ai-platform", "summary": "Claims 132 tok/s Llama 3.1 405B. ArtificialAnalysis later challenged sustained throughput.", "candidate_bill": "Bill_5", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "Cross-vendor variance vs Groq/Cerebras unclear; M1", "_appeared_in_sweeps": [ "sweep_1302" ] }, { "paper_id": "sambanova-llama-405b-cost-vs-h200-2024", "title": "SambaNova 405B per-token cost vs H200 cluster", "authors": [ "Industry analyses" ], "affiliations": [], "country_region": null, "date": "2024-09-15", "venue": "SemiAnalysis-style", "url": "https://www.semianalysis.com", "summary": "SambaNova premium pricing; H200 cluster lower per-token at scale.", "candidate_bill": "Bill_2", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "B2/B7", "_appeared_in_sweeps": [ "sweep_1307" ] }, { "paper_id": "sambanova-sn40l-2024-paper", "title": "SambaNova SN40L: 1.5 TB/s memory bandwidth dataflow chip", "authors": [ "SambaNova Systems" ], "affiliations": [], "country_region": null, "date": "2023-09-19", "venue": "Hot Chips 2023 + SambaNova whitepaper", "url": "https://sambanova.ai/blog/sn40l-chip-the-engine-of-the-sn40l-system", "summary": "Reconfigurable dataflow. Claims Llama 3.1 405B at 132 tok/s on 16 chips.", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "Vendor-supplied; M1", "_appeared_in_sweeps": [ "sweep_1302" ] }, { "paper_id": "sambanova-vs-vllm-fast-api-2024", "title": "SambaNova Fast API vs vLLM-hosting providers", "authors": [ "SambaNova + reviews" ], "affiliations": [], "country_region": null, "date": "2024-11-15", "venue": "Industry", "url": "https://sambanova.ai", "summary": "SambaNova fast API premium; vLLM providers cheaper but more variable.", "candidate_bill": "Bill_7", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "B7", "_appeared_in_sweeps": [ "sweep_1307" ] }, { "paper_id": "sambanova-vs-vllm-tgi-2024", "title": "SambaNova Llama 3.1 405B vs open-stack 405B", "authors": [ "SambaNova + Industry analyses" ], "affiliations": [], "country_region": null, "date": "2024-09-15", "venue": "Various", "url": "https://artificialanalysis.ai", "summary": "SambaNova 132 tok/s vs ~25-40 tok/s for 8x H100 vLLM. ~3x faster, premium pricing.", "candidate_bill": "Bill_7", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "B7 \u2014 closed wins latency, open wins generality", "_appeared_in_sweeps": [ "sweep_1307" ] }, { "paper_id": "sarathi-serve-2024-microsoft-osdi", "title": "Sarathi-Serve: Chunked prefill for LLM serving", "authors": [ "Amey Agrawal", "Nitin Kedia", "Ashish Panwar", "et al." ], "affiliations": [], "country_region": null, "date": "2024-07-15", "venue": "OSDI 2024", "url": "https://arxiv.org/abs/2403.02310", "summary": "Chunked prefill scheduling. Claims 5.6x improvement in 99th percentile TTFT.", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.9, "watchlist_tier": null, "notes": "Peer-reviewed OSDI paper; independent reproduction available", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "scaling-laws-quant-2024-iclr", "title": "Scaling Laws for Precision", "authors": [ "Tanishq Kumar", "Zachary Ankner", "Benjamin Spector", "et al." ], "affiliations": [], "country_region": null, "date": "2024-11-15", "venue": "arXiv 2411.04330", "url": "https://arxiv.org/abs/2411.04330", "summary": "Quantization at low precision degrades larger models more. Counter to vendor claims.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.9, "watchlist_tier": null, "notes": "B8 strong rebuttal", "_appeared_in_sweeps": [ "sweep_1305" ] }, { "paper_id": "scissorhands-kv-2023", "title": "Scissorhands: Exploiting the Persistence of Importance Hypothesis", "authors": [ "Zichang Liu", "Aditya Desai", "Fangshuo Liao", "et al." ], "affiliations": [], "country_region": null, "date": "2023-12-15", "venue": "NeurIPS 2023", "url": "https://arxiv.org/abs/2305.17118", "summary": "Window-based KV cache. Capability tradeoffs documented.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "B6 \u2014 fidelity loss documented", "_appeared_in_sweeps": [ "sweep_1306" ] }, { "paper_id": "self-speculative-2024", "title": "Self-Speculative Decoding", "authors": [ "Jun Zhang", "Jue Wang", "Huan Li", "et al." ], "affiliations": [], "country_region": null, "date": "2024-03-15", "venue": "ACL 2024", "url": "https://arxiv.org/abs/2309.08168", "summary": "Use layer-skipping for draft. No external draft model.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.85, "watchlist_tier": null, "notes": "Peer-reviewed", "_appeared_in_sweeps": [ "sweep_1306" ] }, { "paper_id": "semianalysis-blackwell-2024", "title": "SemiAnalysis: Blackwell shipping reality", "authors": [ "SemiAnalysis" ], "affiliations": [], "country_region": null, "date": "2024-12-15", "venue": "SemiAnalysis", "url": "https://www.semianalysis.com", "summary": "Blackwell scaling pains, shipping reality.", "candidate_bill": "Bill_10", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.85, "watchlist_tier": null, "notes": "B10", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "semianalysis-blackwell-shipping-2024", "title": "SemiAnalysis: Blackwell shipping reality H1 2025", "authors": [ "SemiAnalysis" ], "affiliations": [], "country_region": null, "date": "2025-01-15", "venue": "SemiAnalysis", "url": "https://www.semianalysis.com", "summary": "Real Blackwell shipments lag announcement by 6+ months.", "candidate_bill": "Bill_10", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.9, "watchlist_tier": null, "notes": "B10", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "semianalysis-cerebras-2024", "title": "SemiAnalysis: Cerebras Inference economics", "authors": [ "SemiAnalysis" ], "affiliations": [], "country_region": null, "date": "2024-09-15", "venue": "SemiAnalysis", "url": "https://www.semianalysis.com/p/cerebras-inference-cracking-the-llm", "summary": "Examines Cerebras unit economics; questions sustainability.", "candidate_bill": "Bill_2", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.9, "watchlist_tier": null, "notes": "B2 third-party", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "semianalysis-cerebras-2024-analysis", "title": "SemiAnalysis: Cerebras inference economics", "authors": [ "Dylan Patel et al., SemiAnalysis" ], "affiliations": [], "country_region": null, "date": "2024-09-15", "venue": "SemiAnalysis Substack", "url": "https://www.semianalysis.com/p/cerebras-inference-cracking-the-llm", "summary": "Analysis of cost-per-token economics; raises questions about sustained margins.", "candidate_bill": "Bill_2", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "Third-party analysis; B2 cost-per-token transparency challenged", "_appeared_in_sweeps": [ "sweep_1302" ] }, { "paper_id": "semianalysis-deepseek-h800-2025", "title": "SemiAnalysis: DeepSeek H800 cluster real cost", "authors": [ "SemiAnalysis" ], "affiliations": [], "country_region": null, "date": "2025-02-15", "venue": "SemiAnalysis", "url": "https://www.semianalysis.com/p/deepseek-debates", "summary": "DeepSeek claimed $6M training; actual H800 cluster cost much higher.", "candidate_bill": "Bill_2", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.9, "watchlist_tier": null, "notes": "B2 third-party", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "semianalysis-gpu-cloud-cost-2024", "title": "SemiAnalysis: GPU cloud rental cost analysis", "authors": [ "SemiAnalysis" ], "affiliations": [], "country_region": null, "date": "2024-08-15", "venue": "SemiAnalysis", "url": "https://www.semianalysis.com", "summary": "H100 rental rates by provider. Lambda/CoreWeave/Together/AWS.", "candidate_bill": "Bill_2", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.9, "watchlist_tier": null, "notes": "B2", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "semianalysis-mi300x-2024", "title": "SemiAnalysis: MI300X vs H100 vs H200 deep dive", "authors": [ "Dylan Patel et al., SemiAnalysis" ], "affiliations": [], "country_region": null, "date": "2024-12-15", "venue": "SemiAnalysis Substack", "url": "https://www.semianalysis.com/p/mi300x-vs-h100-vs-h200-benchmark", "summary": "Extensive five-month benchmark study. ROCm immaturity exposed.", "candidate_bill": "Bill_7", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.95, "watchlist_tier": null, "notes": "Strong third-party; closes B7 partially", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "semianalysis-rubin-2025", "title": "SemiAnalysis: NVIDIA Rubin roadmap analysis", "authors": [ "SemiAnalysis" ], "affiliations": [], "country_region": null, "date": "2024-06-15", "venue": "SemiAnalysis", "url": "https://www.semianalysis.com", "summary": "Pre-product Rubin roadmap analysis.", "candidate_bill": "Bill_10", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.75, "watchlist_tier": null, "notes": "Pre-product", "_appeared_in_sweeps": [ "sweep_1308" ] }, { "paper_id": "sglang-radix-blog-2024", "title": "SGLang RadixAttention deep-dive", "authors": [ "LMSYS team" ], "affiliations": [], "country_region": null, "date": "2024-07-25", "venue": "LMSYS blog", "url": "https://lmsys.org/blog/2024-07-25-sglang-llama3/", "summary": "Up to 6.4x throughput on Llama 3 70B for structured/multi-turn workloads.", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "Workload-specific; not generalizable. Cross-vendor variance not measured.", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "sglang-spec-decode-2024", "title": "SGLang speculative decoding support", "authors": [ "LMSYS" ], "affiliations": [], "country_region": null, "date": "2024-09-15", "venue": "SGLang blog", "url": "https://lmsys.org", "summary": "SGLang supports speculative decoding. EAGLE/Medusa integration.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.8, "watchlist_tier": null, "notes": "Mechanism standard", "_appeared_in_sweeps": [ "sweep_1306" ] }, { "paper_id": "sglang-vs-cerebras-2024-llama3.1", "title": "SGLang H100 cluster vs Cerebras Inference", "authors": [ "Independent benchmarks" ], "affiliations": [], "country_region": null, "date": "2024-09-15", "venue": "Modal/Anyscale blogs", "url": "https://modal.com/blog", "summary": "Cerebras single-stream ~3x faster; SGLang multi-tenant 10x cheaper at scale.", "candidate_bill": "Bill_7", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "B7 \u2014 economics fundamentally differ", "_appeared_in_sweeps": [ "sweep_1307" ] }, { "paper_id": "sglang-vs-trt-llm-vs-cerebras-2024", "title": "Cross-framework cross-vendor latency variance", "authors": [ "BentoML / Anyscale" ], "affiliations": [], "country_region": null, "date": "2024-08-15", "venue": "BentoML blog", "url": "https://www.bentoml.com/blog/benchmarking-llm-inference-backends", "summary": "Comprehensive comparison. ~5x variance across configurations.", "candidate_bill": "Bill_5", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.9, "watchlist_tier": null, "notes": "B5/B7 \u2014 variance well above 10%", "_appeared_in_sweeps": [ "sweep_1307" ] }, { "paper_id": "smoothquant-2023-icml", "title": "SmoothQuant: Accurate and Efficient Post-Training Quantization for LLMs", "authors": [ "Guangxuan Xiao", "Ji Lin", "Mickael Seznec", "et al." ], "affiliations": [], "country_region": null, "date": "2023-06-15", "venue": "ICML 2023", "url": "https://arxiv.org/abs/2211.10438", "summary": "W8A8 PTQ. Foundational paper. Fidelity within 1% on Llama 2.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.95, "watchlist_tier": null, "notes": "Peer-reviewed; well-established", "_appeared_in_sweeps": [ "sweep_1305" ] }, { "paper_id": "snapkv-2024", "title": "SnapKV: LLM Knows What You are Looking for", "authors": [ "Yuhong Li", "Yingbing Huang", "Bowen Yang", "et al." ], "affiliations": [], "country_region": null, "date": "2024-04-15", "venue": "NeurIPS 2024", "url": "https://arxiv.org/abs/2404.14469", "summary": "KV cache compression via attention pattern. 3.6x throughput.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.85, "watchlist_tier": null, "notes": "Peer-reviewed", "_appeared_in_sweeps": [ "sweep_1306" ] }, { "paper_id": "spec-bench-2024", "title": "SpecBench: Benchmarking Speculative Decoding Methods", "authors": [ "Heming Xia", "Tao Ge", "Si-Qing Chen", "Furu Wei" ], "affiliations": [], "country_region": null, "date": "2024-07-15", "venue": "arXiv 2401.07851", "url": "https://arxiv.org/abs/2401.07851", "summary": "Benchmark across 6 spec-decode methods on different domains.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.9, "watchlist_tier": null, "notes": "Third-party", "_appeared_in_sweeps": [ "sweep_1306" ] }, { "paper_id": "spec-decode-distillation-medusa2-2024", "title": "Medusa-2: training scheme for draft heads", "authors": [ "Tianle Cai et al." ], "affiliations": [], "country_region": null, "date": "2024-06-15", "venue": "Medusa GitHub", "url": "https://github.com/FasterDecoding/Medusa", "summary": "Improved training for draft heads. Lossless wrt target.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.8, "watchlist_tier": null, "notes": "Academic", "_appeared_in_sweeps": [ "sweep_1306" ] }, { "paper_id": "spec-decode-leviathan-2023", "title": "Fast Inference from Transformers via Speculative Decoding", "authors": [ "Yaniv Leviathan", "Matan Kalman", "Yossi Matias" ], "affiliations": [], "country_region": null, "date": "2023-05-15", "venue": "ICML 2023", "url": "https://arxiv.org/abs/2211.17192", "summary": "Google's speculative decoding paper. 2-3x speedup. Distribution-preserving.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.95, "watchlist_tier": null, "notes": "Peer-reviewed ICML; baseline", "_appeared_in_sweeps": [ "sweep_1306" ] }, { "paper_id": "spec-decode-mod-eagle3-2025", "title": "EAGLE-3 enhanced draft model architecture", "authors": [ "Yuhui Li et al." ], "affiliations": [], "country_region": null, "date": "2025-03-15", "venue": "arXiv 2503.01840", "url": "https://arxiv.org/abs/2503.01840", "summary": "EAGLE-3 improvements with multi-step draft head.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.8, "watchlist_tier": null, "notes": "Academic", "_appeared_in_sweeps": [ "sweep_1306" ] }, { "paper_id": "spec-decode-throughput-2024", "title": "Speculative Decoding Throughput Reality vs Marketing", "authors": [ "Community benchmarks" ], "affiliations": [], "country_region": null, "date": "2024-09-15", "venue": "Anyscale/BentoML blogs", "url": "https://www.bentoml.com", "summary": "Real-world speedups often 1.3-1.7x vs 2-3x claimed.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "Closes B6 \u2014 workload variance", "_appeared_in_sweeps": [ "sweep_1306" ] }, { "paper_id": "specinfer-2024-osdi", "title": "SpecInfer: Tree-based Speculative Inference", "authors": [ "Xupeng Miao", "Gabriele Oliaro", "Zhihao Zhang", "et al." ], "affiliations": [], "country_region": null, "date": "2024-04-15", "venue": "ASPLOS 2024", "url": "https://arxiv.org/abs/2305.09781", "summary": "Tree-based speculative inference with multiple drafts. 2.8x.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.9, "watchlist_tier": null, "notes": "ASPLOS peer-reviewed", "_appeared_in_sweeps": [ "sweep_1306" ] }, { "paper_id": "speculative-decoding-2023-chen", "title": "Accelerating Large Language Model Decoding with Speculative Sampling", "authors": [ "Charlie Chen", "Sebastian Borgeaud", "Geoffrey Irving", "et al." ], "affiliations": [], "country_region": null, "date": "2023-02-15", "venue": "DeepMind tech report", "url": "https://arxiv.org/abs/2302.01318", "summary": "Original speculative sampling. Lossless wrt target distribution.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.95, "watchlist_tier": null, "notes": "Foundational; mathematically lossless", "_appeared_in_sweeps": [ "sweep_1306" ] }, { "paper_id": "speculative-distillation-2024", "title": "Speculative Knowledge Distillation", "authors": [ "Wenda Xu", "Rujun Han", "Zifeng Wang", "et al." ], "affiliations": [], "country_region": null, "date": "2024-10-15", "venue": "arXiv 2410.11325", "url": "https://arxiv.org/abs/2410.11325", "summary": "Distill verifier model for speculative decoding. Lossless wrt target.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.8, "watchlist_tier": null, "notes": "Academic", "_appeared_in_sweeps": [ "sweep_1306" ] }, { "paper_id": "spinquant-2024-meta", "title": "SpinQuant: LLM Quantization with Learned Rotations", "authors": [ "Zechun Liu", "Changsheng Zhao", "Igor Fedorov", "et al. (Meta)" ], "affiliations": [], "country_region": null, "date": "2024-05-15", "venue": "arXiv 2405.16406", "url": "https://arxiv.org/abs/2405.16406", "summary": "Learned rotation for INT4 quant. Llama 3 70B INT4 W4A4 with low drop.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "Meta paper", "_appeared_in_sweeps": [ "sweep_1305" ] }, { "paper_id": "splitwise-2024-microsoft-isca", "title": "Splitwise: Efficient generative LLM inference using phase splitting", "authors": [ "Pratyush Patel", "Esha Choukse", "Chaojie Zhang", "et al." ], "affiliations": [], "country_region": null, "date": "2024-06-15", "venue": "ISCA 2024", "url": "https://arxiv.org/abs/2311.18677", "summary": "Separates prefill and decode onto different hardware. Inspired Dynamo disaggregation.", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.9, "watchlist_tier": null, "notes": "Peer-reviewed ISCA; methodologically sound", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "streaming-llm-2024", "title": "Efficient Streaming Language Models with Attention Sinks", "authors": [ "Guangxuan Xiao", "Yuandong Tian", "Beidi Chen", "et al." ], "affiliations": [], "country_region": null, "date": "2024-03-15", "venue": "ICLR 2024", "url": "https://arxiv.org/abs/2309.17453", "summary": "Attention-sink based streaming inference. Long-context performance tradeoffs.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.85, "watchlist_tier": null, "notes": "Peer-reviewed", "_appeared_in_sweeps": [ "sweep_1306" ] }, { "paper_id": "tencent-int4-bench-2024", "title": "Tencent: Llama 3 INT4 capability ablation", "authors": [ "Tencent / various" ], "affiliations": [], "country_region": null, "date": "2024-06-15", "venue": "Tencent tech blog", "url": "https://github.com/Tencent", "summary": "Reports INT4 keeps MMLU but loses on math reasoning.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.75, "watchlist_tier": null, "notes": "B8 \u2014 capability gap revealed", "_appeared_in_sweeps": [ "sweep_1305" ] }, { "paper_id": "tencent-qingfeng-2024", "title": "Tencent Zixiao AI accelerator", "authors": [ "Tencent" ], "affiliations": [], "country_region": null, "date": "2024-08-15", "venue": "Tencent press", "url": "https://www.tencent.com", "summary": "Internal Tencent inference silicon. Limited public details.", "candidate_bill": "Bill_10", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.7, "watchlist_tier": null, "notes": "Closed shop", "_appeared_in_sweeps": [ "sweep_1303" ] }, { "paper_id": "tensorrt-llm-2024-h100-perf-blog", "title": "TensorRT-LLM v0.10 H100 Performance", "authors": [ "NVIDIA" ], "affiliations": [], "country_region": null, "date": "2024-06-04", "venue": "NVIDIA developer blog", "url": "https://developer.nvidia.com/blog/optimizing-inference-on-llms-with-tensorrt-llm-now-publicly-available/", "summary": "Vendor-published benchmarks for Llama 3 70B on H100. Claims 2x throughput improvement vs prior release.", "candidate_bill": "Bill_9", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.9, "watchlist_tier": null, "notes": "M1 vendor-supplied. No independent measurement against same workload.", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "tensorrt-llm-2025-rls-h200", "title": "TensorRT-LLM Llama 3.1 405B H200 perf", "authors": [ "NVIDIA" ], "affiliations": [], "country_region": null, "date": "2024-09-26", "venue": "NVIDIA blog", "url": "https://developer.nvidia.com/blog/post-training-quantization-of-llama-3-models-with-nvidia-tensorrt-model-optimizer/", "summary": "FP8 quantization of Llama 3.1 405B; claims <1% MMLU degradation.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.75, "watchlist_tier": null, "notes": "Vendor-supplied; M1 disqualification; quantization fidelity beyond MMLU not measured", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "tensorrt-llm-int4-awq-2024", "title": "TensorRT-LLM INT4-AWQ on Llama 3", "authors": [ "NVIDIA" ], "affiliations": [], "country_region": null, "date": "2024-08-01", "venue": "NVIDIA developer blog", "url": "https://developer.nvidia.com/blog/post-training-quantization-of-llama-3-models-with-nvidia-tensorrt-model-optimizer/", "summary": "INT4 weight + INT8 activation; claims <0.5% MMLU drop.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.75, "watchlist_tier": null, "notes": "Vendor-supplied; M1; downstream reasoning eval missing", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "tensorrt-llm-multilora-blog-2024", "title": "TensorRT-LLM Multi-LoRA support", "authors": [ "NVIDIA" ], "affiliations": [], "country_region": null, "date": "2024-05-15", "venue": "NVIDIA dev blog", "url": "https://developer.nvidia.com/blog/tune-and-deploy-lora-llms-with-nvidia-tensorrt-llm/", "summary": "Vendor implementation. Cross-framework variance unknown.", "candidate_bill": "Bill_11", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.7, "watchlist_tier": null, "notes": "M1 vendor-supplied; no matched config across SGLang/vLLM/TRT-LLM", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "tensorrt-llm-vs-vllm-h100-2024", "title": "TRT-LLM vs vLLM head-to-head on H100", "authors": [ "Various community benchmarks" ], "affiliations": [], "country_region": null, "date": "2024-09-15", "venue": "BentoML benchmarks, others", "url": "https://bentoml.com", "summary": "Mixed results. TRT-LLM faster on Llama 3 70B at decode; vLLM competitive elsewhere.", "candidate_bill": "Bill_5", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "B5 \u2014 variance", "_appeared_in_sweeps": [ "sweep_1304" ] }, { "paper_id": "tenstorrent-blackhole-2024", "title": "Tenstorrent Blackhole architecture", "authors": [ "Tenstorrent" ], "affiliations": [], "country_region": null, "date": "2024-09-15", "venue": "Tenstorrent blog", "url": "https://tenstorrent.com/hardware/blackhole", "summary": "Second-gen chip. Targets datacenter inference. No third-party benchmarks yet.", "candidate_bill": "Bill_10", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.8, "watchlist_tier": null, "notes": "Hardware shipping but software stack maturity gap", "_appeared_in_sweeps": [ "sweep_1302" ] }, { "paper_id": "tenstorrent-blackhole-arxiv-2025", "title": "Tenstorrent open source kernels for transformer inference", "authors": [ "Tenstorrent engineering" ], "affiliations": [], "country_region": null, "date": "2025-01-15", "venue": "GitHub + blogs", "url": "https://github.com/tenstorrent/tt-metal", "summary": "Open RISC-V tensor kernel framework. Llama support preview only.", "candidate_bill": "Bill_7", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.65, "watchlist_tier": null, "notes": "Promising but unverified at scale", "_appeared_in_sweeps": [ "sweep_1302" ] }, { "paper_id": "tenstorrent-wormhole-2024-launch", "title": "Tenstorrent Wormhole and Grayskull launch", "authors": [ "Tenstorrent / Jim Keller" ], "affiliations": [], "country_region": null, "date": "2024-07-15", "venue": "Tenstorrent docs", "url": "https://tenstorrent.com/hardware/wormhole", "summary": "RISC-V + tensor cores. Claims competitive perf-per-dollar; limited LLM benchmarks.", "candidate_bill": "Bill_7", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.75, "watchlist_tier": null, "notes": "Limited published benchmarks; cross-vendor matched comparison absent", "_appeared_in_sweeps": [ "sweep_1302" ] }, { "paper_id": "tgi-huggingface-2024-rls", "title": "HuggingFace TGI release notes", "authors": [ "HuggingFace" ], "affiliations": [], "country_region": null, "date": "2024-10-01", "venue": "TGI GitHub", "url": "https://github.com/huggingface/text-generation-inference", "summary": "Production inference server. Limited cross-framework benchmarks against vLLM/SGLang.", "candidate_bill": "Bill_5", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.7, "watchlist_tier": null, "notes": "Cross-vendor stability not published", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "tgi-quant-cookbook-2024", "title": "TGI quantization cookbook", "authors": [ "HuggingFace TGI" ], "affiliations": [], "country_region": null, "date": "2024-08-15", "venue": "HF docs", "url": "https://huggingface.co/docs/text-generation-inference", "summary": "TGI supports EETQ/GPTQ/AWQ. Latency claims; fidelity not deeply audited.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.75, "watchlist_tier": null, "notes": "B8 \u2014 vendor cookbook", "_appeared_in_sweeps": [ "sweep_1305" ] }, { "paper_id": "tinygrad-comma-amd-2024", "title": "Tinygrad George Hotz AMD test reports", "authors": [ "George Hotz / tinygrad" ], "affiliations": [], "country_region": null, "date": "2024-09-15", "venue": "tinygrad GitHub", "url": "https://github.com/tinygrad/tinygrad", "summary": "Reports ROCm driver instability vs claimed AMD perf.", "candidate_bill": "Bill_7", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "B7 \u2014 vendor-closed at matched compute reveals real-world gap", "_appeared_in_sweeps": [ "sweep_1304" ] }, { "paper_id": "tpu-jax-pallas-2024", "title": "Pallas: JAX kernels for TPU", "authors": [ "Google DeepMind" ], "affiliations": [], "country_region": null, "date": "2024-04-15", "venue": "JAX docs", "url": "https://jax.readthedocs.io/en/latest/pallas/index.html", "summary": "Low-level TPU kernel API. Not portable to GPU.", "candidate_bill": "Bill_11", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "Platform-lock", "_appeared_in_sweeps": [ "sweep_1303" ] }, { "paper_id": "tpu-pathways-2024-deepmind", "title": "Pathways system for TPU scaling", "authors": [ "Google DeepMind" ], "affiliations": [], "country_region": null, "date": "2024-05-15", "venue": "DeepMind blog", "url": "https://deepmind.google", "summary": "Pathways system enables Gemini at scale on TPU. Closed.", "candidate_bill": "Bill_11", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "Platform-lock", "_appeared_in_sweeps": [ "sweep_1303" ] }, { "paper_id": "tpu-v5p-2023-google-launch", "title": "Google TPU v5p launch", "authors": [ "Google Cloud" ], "affiliations": [], "country_region": null, "date": "2023-12-06", "venue": "Google Cloud blog", "url": "https://cloud.google.com/blog/products/ai-machine-learning/introducing-cloud-tpu-v5p-and-ai-hypercomputer", "summary": "TPU v5p for Gemini training/inference. Internal benchmarks only.", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "Vendor-only; M5 platform-lock", "_appeared_in_sweeps": [ "sweep_1303" ] }, { "paper_id": "tpu-v5p-gemini-cost-2024", "title": "Google Gemini per-token cost TPU advantage", "authors": [ "industry analysts" ], "affiliations": [], "country_region": null, "date": "2024-06-15", "venue": "SemiAnalysis + analyses", "url": "https://www.semianalysis.com", "summary": "Estimates of TPU cost advantage; not verifiable independently.", "candidate_bill": "Bill_2", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.7, "watchlist_tier": null, "notes": "B2 transparency missing", "_appeared_in_sweeps": [ "sweep_1303" ] }, { "paper_id": "tpu-v6e-trillium-2024", "title": "Google Trillium (TPU v6e) launch", "authors": [ "Google Cloud" ], "affiliations": [], "country_region": null, "date": "2024-05-14", "venue": "Google I/O 2024", "url": "https://cloud.google.com/blog/products/compute/introducing-trillium-6th-gen-tpus", "summary": "4.7x compute vs v5e. Inference-optimized.", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "Vendor-supplied; M1", "_appeared_in_sweeps": [ "sweep_1303" ] }, { "paper_id": "tpu-v7-ironwood-2025", "title": "Google Ironwood TPU (v7) at Google Cloud Next 2025", "authors": [ "Google Cloud" ], "affiliations": [], "country_region": null, "date": "2025-04-09", "venue": "Google Cloud Next 2025", "url": "https://cloud.google.com/blog/products/compute/whats-new-with-ai-hypercomputer", "summary": "Inference-only TPU. Claims 5x perf vs v5p. 9216-chip pods.", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "Vendor-only; cross-cloud variance unmeasured", "_appeared_in_sweeps": [ "sweep_1303" ] }, { "paper_id": "tpu-vs-h100-llama-2024-bench", "title": "TPU v5p vs H100 Llama 2 70B cross-cloud test", "authors": [ "Independent benchmark teams" ], "affiliations": [], "country_region": null, "date": "2024-08-15", "venue": "MosaicML, Modular blog", "url": "https://www.databricks.com/blog", "summary": "Indicates variance 2-3x across clouds at matched cost basis.", "candidate_bill": "Bill_5", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.75, "watchlist_tier": null, "notes": "B5 \u2014 cross-vendor stability fails", "_appeared_in_sweeps": [ "sweep_1303" ] }, { "paper_id": "tpu-vs-h100-mlperf-2024", "title": "MLPerf Inference TPU vs H100 head-to-head", "authors": [ "MLCommons" ], "affiliations": [], "country_region": null, "date": "2024-09-04", "venue": "MLPerf", "url": "https://mlcommons.org", "summary": "Direct submitter-to-submitter comparison constrained by MLPerf workload definition.", "candidate_bill": "Bill_9", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.75, "watchlist_tier": null, "notes": "M3 \u2014 workload mismatch with real production", "_appeared_in_sweeps": [ "sweep_1303" ] }, { "paper_id": "trainium2-anthropic-rainier-2025", "title": "Project Rainier: 400K Trainium2 build", "authors": [ "AWS + Anthropic" ], "affiliations": [], "country_region": null, "date": "2025-03-15", "venue": "AWS blog", "url": "https://aws.amazon.com", "summary": "Large-scale Trainium2 deployment. Independent throughput data not public.", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.75, "watchlist_tier": null, "notes": "Procurement; no benchmark", "_appeared_in_sweeps": [ "sweep_1303" ] }, { "paper_id": "trainium2-llm-benchmarks-aa-2025", "title": "ArtificialAnalysis Trainium2 Llama 3.3 benchmark", "authors": [ "ArtificialAnalysis.ai" ], "affiliations": [], "country_region": null, "date": "2025-04-15", "venue": "ArtificialAnalysis.ai", "url": "https://artificialanalysis.ai", "summary": "Independent measurement of Trainium2-hosted Llama 3.3 70B.", "candidate_bill": "Bill_5", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "Third-party measurement, partially closes B5", "_appeared_in_sweeps": [ "sweep_1303" ] }, { "paper_id": "trillium-claude-2024-blog", "title": "Anthropic Claude on Google TPU Trillium reports", "authors": [ "Google + Anthropic" ], "affiliations": [], "country_region": null, "date": "2024-10-30", "venue": "Google Cloud blog", "url": "https://cloud.google.com/blog/products/ai-machine-learning/anthropic-deepens-partnership-with-google-cloud", "summary": "Anthropic uses TPUs for training/inference. Per-platform cost-per-token not public.", "candidate_bill": "Bill_2", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "B2 transparency challenged", "_appeared_in_sweeps": [ "sweep_1303" ] }, { "paper_id": "triton-server-2024-mlperf-results", "title": "Triton Inference Server v24.x MLPerf submissions", "authors": [ "NVIDIA" ], "affiliations": [], "country_region": null, "date": "2024-09-04", "venue": "MLPerf Inference v4.1", "url": "https://mlcommons.org/benchmarks/inference-datacenter/", "summary": "NVIDIA-submitted MLPerf results for Llama-2-70B server scenario.", "candidate_bill": "Bill_9", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.7, "watchlist_tier": null, "notes": "MLPerf format constrains workload; vendor submitter problem (M3 partially)", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "trt-llm-spec-decode-2024", "title": "TensorRT-LLM speculative decoding for Medusa/EAGLE", "authors": [ "NVIDIA" ], "affiliations": [], "country_region": null, "date": "2024-09-15", "venue": "NVIDIA blog", "url": "https://developer.nvidia.com/blog/optimizing-llm-inference-for-low-latency-applications/", "summary": "TRT-LLM supports speculative decoding. Vendor-supplied benchmarks.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "Vendor implementation", "_appeared_in_sweeps": [ "sweep_1306" ] }, { "paper_id": "trt-vs-vllm-mi300x-2024", "title": "MI300X vLLM ROCm gap analysis", "authors": [ "Hot Aisle Inc." ], "affiliations": [], "country_region": null, "date": "2024-10-15", "venue": "Hot Aisle blog", "url": "https://hotaisle.xyz", "summary": "Third-party MI300X cloud provider analysis.", "candidate_bill": "Bill_5", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "B5", "_appeared_in_sweeps": [ "sweep_1304" ] }, { "paper_id": "vllm-2024-paged-attention-v0.6", "title": "vLLM v0.6: 2.7x throughput improvement", "authors": [ "vLLM team" ], "affiliations": [], "country_region": null, "date": "2024-09-05", "venue": "vLLM blog + GitHub release notes", "url": "https://blog.vllm.ai/2024/09/05/perf-update.html", "summary": "Major scheduling rewrite, TTFT improvements with claims of 2.7x throughput at matched latency.", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "Self-reported; no independent reproduction under matched closed-vendor workload", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "vllm-2024-prefix-caching", "title": "vLLM prefix caching feature analysis", "authors": [ "vLLM team" ], "affiliations": [], "country_region": null, "date": "2024-05-15", "venue": "vLLM docs", "url": "https://docs.vllm.ai/en/latest/automatic_prefix_caching/apc.html", "summary": "Automatic prefix caching for shared system prompts. Throughput claims workload-dependent.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.85, "watchlist_tier": null, "notes": "Reasonable claim, mechanism validated independently", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "vllm-async-engine-2025", "title": "vLLM v1 AsyncEngine architecture", "authors": [ "vLLM team" ], "affiliations": [], "country_region": null, "date": "2025-01-27", "venue": "vLLM blog", "url": "https://blog.vllm.ai/2025/01/27/v1-alpha-release.html", "summary": "Lock-free async engine. Claims latency reductions; independent reproduction limited.", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.75, "watchlist_tier": null, "notes": "M3 vendor-self-eval", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "vllm-cost-trans-2024", "title": "vLLM open-source cost transparency", "authors": [ "vLLM community" ], "affiliations": [], "country_region": null, "date": "2024-09-15", "venue": "vLLM docs", "url": "https://docs.vllm.ai", "summary": "Self-hosted vLLM cost depends on GPU rental. Per-token deeply transparent.", "candidate_bill": "Bill_2", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.85, "watchlist_tier": null, "notes": "B2 \u2014 open wins transparency", "_appeared_in_sweeps": [ "sweep_1307" ] }, { "paper_id": "vllm-fp8-2024-ampere-hopper", "title": "vLLM FP8 quantization support", "authors": [ "vLLM team" ], "affiliations": [], "country_region": null, "date": "2024-06-15", "venue": "vLLM blog", "url": "https://blog.vllm.ai/2024/05/14/vllm-fp8.html", "summary": "Native FP8 on H100/H200. Claims <1% MMLU drop.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.75, "watchlist_tier": null, "notes": "Benchmark-only quantization fidelity; reasoning eval not in scope", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "vllm-fp8-mmlu-blog-2024", "title": "Neural Magic FP8 MMLU on Llama 3 405B", "authors": [ "Neural Magic" ], "affiliations": [], "country_region": null, "date": "2024-09-01", "venue": "Neural Magic blog", "url": "https://neuralmagic.com/blog/we-ran-over-half-a-million-evaluations-on-quantized-llms-heres-what-we-found/", "summary": "500K evals showing W8A8 retains 99% MMLU; INT4 ~96-97%.", "candidate_bill": "Bill_8", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "Vendor-supplied but extensive eval; B8 partially closed but reasoning gap remains", "_appeared_in_sweeps": [ "sweep_1305" ] }, { "paper_id": "vllm-h200-2024-update", "title": "vLLM H200 + FP8 Llama 3.1 405B blog", "authors": [ "vLLM team + NVIDIA" ], "affiliations": [], "country_region": null, "date": "2024-10-22", "venue": "vLLM blog", "url": "https://blog.vllm.ai/2024/10/17/spec-decode.html", "summary": "Llama 3.1 405B FP8 on H200; documents throughput vs H100 only.", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.75, "watchlist_tier": null, "notes": "Vendor-friendly; no cross-vendor matched comparison", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "vllm-h200-vs-groq-llama3.3-2024", "title": "vLLM H200 Llama 3.3 70B vs Groq", "authors": [ "Industry" ], "affiliations": [], "country_region": null, "date": "2024-12-15", "venue": "AA", "url": "https://artificialanalysis.ai", "summary": "Groq still 3-5x faster at single-stream; vLLM cheaper at scale.", "candidate_bill": "Bill_7", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "B7", "_appeared_in_sweeps": [ "sweep_1307" ] }, { "paper_id": "vllm-mlperf-vs-groq-mlperf-2024", "title": "MLPerf Inference v4.1 \u2014 vLLM submissions vs Groq", "authors": [ "MLCommons" ], "affiliations": [], "country_region": null, "date": "2024-09-04", "venue": "MLPerf", "url": "https://mlcommons.org/benchmarks/inference-datacenter/", "summary": "Groq did not participate; vLLM submissions only via NVIDIA/AMD/AWS.", "candidate_bill": "Bill_9", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "B7/B9 \u2014 closed vendors avoid standardized benchmarks", "_appeared_in_sweeps": [ "sweep_1307" ] }, { "paper_id": "vllm-mooncake-2024", "title": "Mooncake: A KVCache-centric architecture", "authors": [ "Ruoyu Qin", "Zheming Li", "Weiran He", "et al. (Moonshot)" ], "affiliations": [], "country_region": null, "date": "2024-06-24", "venue": "arXiv 2407.00079", "url": "https://arxiv.org/abs/2407.00079", "summary": "Disaggregated KV cache pooling. Production scale at Moonshot AI Kimi.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.85, "watchlist_tier": null, "notes": "Production paper; cross-framework portability claims unverified", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "vllm-multilora-punica-2024", "title": "Punica: Multi-LoRA serving", "authors": [ "Lequn Chen", "Zihao Ye", "et al." ], "affiliations": [], "country_region": null, "date": "2024-03-12", "venue": "MLSys 2024", "url": "https://arxiv.org/abs/2310.18547", "summary": "Multi-tenant LoRA serving. Independent academic. Cross-vendor not measured.", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.85, "watchlist_tier": null, "notes": "Peer-reviewed MLSys", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "vllm-multinode-2024-disagg", "title": "vLLM disaggregated prefill (experimental)", "authors": [ "vLLM team" ], "affiliations": [], "country_region": null, "date": "2025-02-15", "venue": "vLLM docs", "url": "https://docs.vllm.ai/en/latest/design/v1/prefix_caching.html", "summary": "Experimental disaggregated mode. No public throughput-at-matched-latency numbers.", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.6, "watchlist_tier": null, "notes": "G2 \u2014 not enough public data", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "vllm-spec-decode-2024", "title": "vLLM speculative decoding production support", "authors": [ "vLLM team" ], "affiliations": [], "country_region": null, "date": "2024-10-17", "venue": "vLLM blog", "url": "https://blog.vllm.ai/2024/10/17/spec-decode.html", "summary": "Production EAGLE + ngram + draft model speculative. Up to 1.5x speedup.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "Vendor-supplied; mechanism is mathematically lossless", "_appeared_in_sweeps": [ "sweep_1306" ] }, { "paper_id": "vllm-v1-2025-engine-rewrite", "title": "vLLM v1: Major engine rewrite", "authors": [ "vLLM team" ], "affiliations": [], "country_region": null, "date": "2025-01-27", "venue": "vLLM blog", "url": "https://blog.vllm.ai/2025/01/27/v1-alpha-release.html", "summary": "Async scheduler, persistent batching. Claims 1.7x speedup over v0.6.", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "Self-reported; independent reproduction limited at release", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "vllm-vs-groq-aa-llama3-2024", "title": "ArtificialAnalysis vLLM-providers vs Groq head-to-head", "authors": [ "ArtificialAnalysis.ai" ], "affiliations": [], "country_region": null, "date": "2024-08-15", "venue": "ArtificialAnalysis.ai", "url": "https://artificialanalysis.ai/models/llama-3-3-instruct-70b/providers", "summary": "Groq significantly faster at low batch; vLLM-providers more stable at high concurrency.", "candidate_bill": "Bill_7", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.9, "watchlist_tier": null, "notes": "B7 \u2014 direct strong-baseline comparison; closed wins on latency, open wins on stability", "_appeared_in_sweeps": [ "sweep_1307" ] }, { "paper_id": "vllm-vs-sglang-2024-llm-d-blog", "title": "LLM-d: K8s inference scheduling layer", "authors": [ "Red Hat, Google, IBM, NVIDIA" ], "affiliations": [], "country_region": null, "date": "2025-05-01", "venue": "llm-d.ai launch", "url": "https://llm-d.ai", "summary": "Cross-framework K8s scheduling. Cross-vendor matched-workload measurements forthcoming.", "candidate_bill": "Bill_11", "candidate_meta_cost": null, "verdict": "needs_gate", "confidence": 0.6, "watchlist_tier": null, "notes": "Brand-new project; promises cross-framework but no data yet", "_appeared_in_sweeps": [ "sweep_1301" ] }, { "paper_id": "vllm-vs-sglang-h100-2024", "title": "vLLM v0.6 vs SGLang on H100 Llama 3 70B", "authors": [ "LMSYS" ], "affiliations": [], "country_region": null, "date": "2024-09-15", "venue": "LMSYS blog", "url": "https://lmsys.org/blog/2024-07-25-sglang-llama3/", "summary": "SGLang faster on structured workloads; vLLM faster on simple workloads.", "candidate_bill": "Bill_5", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "B5 \u2014 within open-stack variance", "_appeared_in_sweeps": [ "sweep_1307" ] }, { "paper_id": "vllm-vs-tensorrt-llm-h200-2024", "title": "vLLM v0.6 vs TensorRT-LLM on H200", "authors": [ "BentoML" ], "affiliations": [], "country_region": null, "date": "2024-10-15", "venue": "BentoML blog", "url": "https://www.bentoml.com/blog/benchmarking-llm-inference-backends", "summary": "TRT-LLM 1.2-1.5x faster than vLLM at low concurrency. Reversed at high.", "candidate_bill": "Bill_5", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.85, "watchlist_tier": null, "notes": "B5 \u2014 workload-dependent", "_appeared_in_sweeps": [ "sweep_1307" ] }, { "paper_id": "vllm-vs-tgi-mixtral-2024", "title": "vLLM vs TGI Mixtral 8x7B on H100", "authors": [ "BentoML" ], "affiliations": [], "country_region": null, "date": "2024-06-15", "venue": "BentoML blog", "url": "https://www.bentoml.com/blog/benchmarking-llm-inference-backends", "summary": "Roughly equivalent at high concurrency; vLLM slightly better TTFT.", "candidate_bill": "Bill_5", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.8, "watchlist_tier": null, "notes": "B5", "_appeared_in_sweeps": [ "sweep_1307" ] }, { "paper_id": "wormhole-llm-tt-metal-2024", "title": "Tenstorrent Metalium LLM kernels", "authors": [ "Tenstorrent" ], "affiliations": [], "country_region": null, "date": "2024-11-15", "venue": "Tenstorrent docs + GitHub", "url": "https://github.com/tenstorrent/tt-metal", "summary": "Open kernels for Wormhole; Llama 3 70B preview. No matched cross-vendor data.", "candidate_bill": "Bill_7", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.7, "watchlist_tier": null, "notes": "Open source but immature stack; matched comparison missing", "_appeared_in_sweeps": [ "sweep_1302" ] }, { "paper_id": "yoco-kvcache-2024", "title": "YOCO: You Only Cache Once", "authors": [ "Yutao Sun", "Li Dong", "Yi Zhu", "et al." ], "affiliations": [], "country_region": null, "date": "2024-05-15", "venue": "arXiv 2405.05254", "url": "https://arxiv.org/abs/2405.05254", "summary": "Cross-decoder KV-cache reuse. 6.4x throughput.", "candidate_bill": "Bill_6", "candidate_meta_cost": null, "verdict": "known_bill", "confidence": 0.85, "watchlist_tier": null, "notes": "Microsoft research", "_appeared_in_sweeps": [ "sweep_1306" ] }, { "paper_id": "zheng-2024-sglang-arxiv", "title": "SGLang: Efficient Execution of Structured Language Model Programs", "authors": [ "Lianmin Zheng", "Liangsheng Yin", "Zhiqiang Xie", "et al." ], "affiliations": [], "country_region": null, "date": "2024-12-05", "venue": "NeurIPS 2024", "url": "https://arxiv.org/abs/2312.07104", "summary": "RadixAttention and structured generation. Claims 6.4x throughput vs Guidance baseline.", "candidate_bill": "Bill_1", "candidate_meta_cost": null, "verdict": "rebuttal_paper", "confidence": 0.9, "watchlist_tier": null, "notes": "Strong baseline disclosure; cross-vendor variance not directly measured", "_appeared_in_sweeps": [ "sweep_1301" ] } ]