Datasets:
File size: 18,044 Bytes
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"name": "AgentPerfBench",
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"description": "Per-kernel Nsight Compute (ncu) hardware counter profiles. Individual kernel invocations across 4 GPUs and 13 model/sweep sources.",
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"rai:dataLimitations": "Results cover NVIDIA H100, A100, RTX 3090, and RTX 2080 Ti GPUs only and may not generalize to other accelerators (AMD, Intel, TPU). Benchmark configurations are pinned to vLLM 0.19.0 and SGLang 0.5.9; results do not represent other engine versions. Concurrency levels (1-320) may not cover extreme-scale deployments. Not recommended as sole basis for hardware purchasing decisions or for comparing model task quality.",
"rai:dataBiases": "Model selection over-represents Meta Llama and Alibaba Qwen families. Hardware is exclusively NVIDIA GPUs. Workload profiles are author-designed approximations of production traffic; real deployment patterns may differ.",
"rai:personalSensitiveInformation": "No personally identifiable information is present. All API endpoints and credentials are stripped. Workload traces use synthetic random tokens or publicly available coding benchmarks.",
"rai:dataUseCases": "Established uses: relative comparison of inference engine throughput, latency benchmarking under controlled conditions, TTFT scaling with context length in multi-turn sessions, per-kernel performance modelling. Not established: absolute latency prediction for production, model quality comparison, cost estimation.",
"rai:dataSocialImpact": "Enables reproducible comparison of open-source LLM serving systems, supporting infrastructure research and reducing vendor lock-in.",
"rai:hasSyntheticData": true,
"prov:wasDerivedFrom": [
{
"@id": "https://huggingface.co/datasets/princeton-nlp/SWE-bench",
"name": "SWE-Bench"
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"name": "TerminalBench"
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{
"@id": "https://github.com/xlang-ai/OSWorld",
"name": "OSWorld"
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{
"@id": "https://huggingface.co/datasets/Aeala/ShareGPT_Vicuna_unfiltered",
"name": "ShareGPT"
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"name": "AgentPerfBench benchmark collection",
"description": "Deploy model on target GPU with specified engine and tensor parallelism. Send requests per configuration after warmup using closed-loop concurrency control. Record per-request TTFT, TPOT, ITL, E2EL, and token counts. Compute summary statistics. Sanitize credentials and convert to Parquet."
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"rai:dataCollection": "Automated benchmark collection. Models deployed on target GPU with specified engine and tensor parallelism. Requests sent per workload profile using closed-loop concurrency control with semaphore. Per-request TTFT, TPOT, ITL, E2EL, and token counts recorded. Kernel profiles collected via Nsight Compute (ncu) with hardware counter metrics.",
"rai:dataCollectionType": "Automated measurement via benchmarking scripts. No human subjects or crowdsourcing.",
"rai:dataCollectionMissingData": "Some model-hardware-concurrency combinations are absent due to OOM or engine incompatibility. Concurrency levels exceeding the session-pool size were filtered (see Concurrency filtering in README).",
"rai:dataPreprocessing": "Credentials and API endpoints stripped. Concurrency-buggy rows filtered. Configurations with completion rates below 75% are excluded. Summary statistics (mean, median, p90, p99) computed from per-request measurements. Kernel names demangled from NCU output.",
"rai:dataAnnotationProtocol": "Not applicable. Fully automated benchmark collection with no human annotation.",
"rai:dataAnnotationPlatform": "Not applicable. No human annotation.",
"rai:dataAnnotationAnalysis": "Not applicable. No human annotation."
}
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