README.md

metadata

license: apache-2.0
task_categories:
  - tabular-regression
language:
  - en
tags:
  - llm-inference
  - benchmarking
  - gpu-profiling
  - vllm
  - sglang
  - agentic-workloads
size_categories:
  - 1K<n<10K
pretty_name: AgentPerfBench
version: '1.0'
configs:
  - config_name: trace_replay
    data_files:
      - split: summary
        path: trace_replay/summary.parquet
  - config_name: distributional
    data_files:
      - split: summary
        path: distributional/summary.parquet
dataset_info:
  - config_name: trace_replay
    features:
      - name: run_id
        dtype: string
      - name: model
        dtype: string
      - name: model_family
        dtype: string
      - name: hardware
        dtype: string
      - name: engine
        dtype: string
      - name: tensor_parallelism
        dtype: int64
      - name: profile
        dtype: string
      - name: concurrency
        dtype: int64
      - name: num_requests
        dtype: int64
      - name: duration_s
        dtype: float64
      - name: successful_requests
        dtype: int64
      - name: failed_requests
        dtype: int64
      - name: request_throughput
        dtype: float64
      - name: input_token_throughput
        dtype: float64
      - name: output_token_throughput
        dtype: float64
      - name: total_token_throughput
        dtype: float64
      - name: mean_ttft_ms
        dtype: float64
      - name: median_ttft_ms
        dtype: float64
      - name: p90_ttft_ms
        dtype: float64
      - name: p99_ttft_ms
        dtype: float64
      - name: mean_tpot_ms
        dtype: float64
      - name: median_tpot_ms
        dtype: float64
      - name: p90_tpot_ms
        dtype: float64
      - name: p99_tpot_ms
        dtype: float64
      - name: mean_itl_ms
        dtype: float64
      - name: median_itl_ms
        dtype: float64
      - name: p90_itl_ms
        dtype: float64
      - name: p99_itl_ms
        dtype: float64
      - name: mean_e2el_ms
        dtype: float64
      - name: median_e2el_ms
        dtype: float64
      - name: p90_e2el_ms
        dtype: float64
      - name: p99_e2el_ms
        dtype: float64
    splits:
      - name: summary
        num_rows: 3147
        num_bytes: 694254
  - config_name: distributional
    features:
      - name: run_id
        dtype: string
      - name: model
        dtype: string
      - name: model_family
        dtype: string
      - name: hardware
        dtype: string
      - name: engine
        dtype: string
      - name: tensor_parallelism
        dtype: int64
      - name: profile
        dtype: string
      - name: concurrency
        dtype: int64
      - name: num_requests
        dtype: int64
      - name: duration_s
        dtype: float64
      - name: successful_requests
        dtype: int64
      - name: failed_requests
        dtype: int64
      - name: request_throughput
        dtype: float64
      - name: input_token_throughput
        dtype: float64
      - name: output_token_throughput
        dtype: float64
      - name: total_token_throughput
        dtype: float64
      - name: mean_ttft_ms
        dtype: float64
      - name: median_ttft_ms
        dtype: float64
      - name: p90_ttft_ms
        dtype: float64
      - name: p99_ttft_ms
        dtype: float64
      - name: mean_tpot_ms
        dtype: float64
      - name: median_tpot_ms
        dtype: float64
      - name: p90_tpot_ms
        dtype: float64
      - name: p99_tpot_ms
        dtype: float64
      - name: mean_itl_ms
        dtype: float64
      - name: median_itl_ms
        dtype: float64
      - name: p90_itl_ms
        dtype: float64
      - name: p99_itl_ms
        dtype: float64
      - name: mean_e2el_ms
        dtype: float64
      - name: median_e2el_ms
        dtype: float64
      - name: p90_e2el_ms
        dtype: float64
      - name: p99_e2el_ms
        dtype: float64
    splits:
      - name: summary
        num_rows: 245
        num_bytes: 70836

AgentPerfBench

LLM inference benchmark: 3,392 runs measuring TTFT, TPOT, ITL, and throughput across 9 models, up to 14 GPU configurations, and 2 serving engines (vLLM 0.19.0, SGLang 0.5.9). All models served in BF16 except gpt-oss, which uses mxfp4 for projection weights.

Dataset configurations

The dataset provides two configurations. trace_replay replays exact input/output sequences from recorded agent sessions. distributional samples from statistical distributions fitted to those same workloads, trading fidelity for faster sweeps across the hardware matrix.

trace_replay (3,147 rows)

Replays exact ISL/OSL sequences from recorded agent sessions (SWE-Bench, TerminalBench, OSWorld, ShareGPT). Covers 77 unique (model, hardware, engine) combinations across 17 profiles and 6 concurrency levels. The full 5-dimensional matrix is 12.2% filled; not all models run on all hardware.

17 profiles: chat-medium, chat-multiturn-long, chat-multiturn-medium, chat-multiturn-short, chat-short, chat-singleturn, coding-singleturn, decode-heavy, osworld-multiturn-long, osworld-multiturn-medium, osworld-multiturn-short, prefill-heavy, random-1k, swebench-multiturn-medium, swebench-multiturn-short, terminalbench-multiturn-medium, terminalbench-multiturn-short

distributional (245 rows)

Samples ISL/OSL from lognormal distributions fitted to real workload statistics. Covers 42 unique (model, hardware, engine) combinations across 6 profiles and 7 concurrency levels (3.0% matrix fill). gpt-oss-120b, 3090x8, and A100-40GBx8 are excluded from this configuration.

6 profiles: chat-multiturn, chat-singleturn, coding-singleturn, osworld-multiturn, swebench-multiturn, terminalbench-multiturn

Concurrency filtering

The benchmark harness capped actual concurrent connections at the session pool size. Rows where declared concurrency exceeded the pool were excluded:

• trace_replay: concurrency > 100 removed (session pool was 100). Remaining values: {1, 5, 10, 20, 40, 80}.
• distributional (pre-fix): concurrency > 10 removed (session pool was 10). Post-fix data has no cap. Remaining values: {1, 5, 10, 40, 80, 200, 320}.

Config	Rows
trace_replay	3,147
distributional	245
Total	3,392

Failed requests

Some runs produce request failures, typically at high concurrency where the engine hits memory or timeout limits. 30.8% of trace_replay rows and 42% of distributional rows have failed_requests > 0. Summary metrics (TTFT, TPOT, throughput) are computed from successful requests only.

Coverage

Hardware

All benchmarks collected on PyTorch 2.10.0, CUDA 12.8.

GPU	VRAM	HBM bandwidth	Peak half-precision TFLOPS
NVIDIA H100 SXM	80 GB	3.35 TB/s	989
NVIDIA A100 SXM4	40 GB	1.56 TB/s	312
NVIDIA RTX 3090	24 GB	936 GB/s	71
NVIDIA RTX 2080 Ti	11 GB	616 GB/s	27

Multi-GPU configurations: 1, 2, 4, or 8 GPUs with tensor parallelism. TP degree depends on model size and available GPUs.

Models

All models served in BF16 unless noted.

Model	Family	Parameters	Architecture	Notes
Llama-3.1-8B	Llama	8B	Dense
Llama-3.1-70B	Llama	70B	Dense
Llama-3.3-70B	Llama	70B	Dense
Qwen2.5-72B	Qwen	72B	Dense
Qwen3.5-9B	Qwen	9B	Dense
Qwen3.5-27B	Qwen	27B	Dense
Mixtral-8x7B	Mixtral	46.7B (12.9B active)	MoE
gpt-oss-20b	GPT-OSS	21B (3.6B active)	MoE	mxfp4 projections
gpt-oss-120b	GPT-OSS	117B (5.1B active)	MoE	mxfp4 projections

Model names in this table match the model column in the parquet files.

Engines

• vLLM 0.19.0
• SGLang 0.5.9

Schema

Each row in summary.parquet (both configs):

Column	Type	Description
run_id	string	Deterministic hash of run parameters
model	string	Model short name
model_family	string	Model family (llama, qwen, gpt-oss, mixtral)
hardware	string	GPU configuration (e.g., H100x4)
engine	string	Serving engine (vllm, sglang)
tensor_parallelism	int	TP degree
profile	string	Workload profile name
concurrency	int	Concurrent request level
num_requests	int	Total requests in run
duration_s	float	Total run duration
successful_requests	int	Completed requests
failed_requests	int	Failed requests
request_throughput	float	Requests/second
input_token_throughput	float	Input tokens/second
output_token_throughput	float	Output tokens/second
total_token_throughput	float	Total tokens/second
mean/median/p90/p99_ttft_ms	float	Time to first token
mean/median/p90/p99_tpot_ms	float	Time per output token
mean/median/p90/p99_itl_ms	float	Inter-token latency
mean/median/p90/p99_e2el_ms	float	End-to-end latency

Loading

from datasets import load_dataset

ds = load_dataset("agent-perf-bench/AgentPerfBench", "trace_replay")
# or "distributional"

Benchmark methodology

• Closed-loop concurrency with semaphore control.
• Concurrency levels: {1, 5, 10, 20, 40, 80} (trace_replay), {1, 5, 10, 40, 80, 200, 320} (distributional).
• 3-request warmup before each configuration.
• Metrics: TTFT, TPOT, ITL, E2EL, request throughput, token throughput.
• Summary statistics: mean, median, p90, p99.
• Collection period: March 2026 onwards.
• PyTorch 2.10.0, CUDA 12.8 on all machines. All models served in BF16 (gpt-oss: mxfp4 projection weights).

Future releases

• Per-request and multi-turn granularity data (pending raw JSON availability from collection infrastructure).
• Per-kernel CUDA roofline profiles (PyTorch profiler, 2-layer forward passes, batch sizes 1/4/8/32/64).
• This is version 1.0. Updates will be tagged with semantic versions.

Intended uses

• Inference engine comparison under controlled conditions.
• Capacity planning for LLM deployments.
• TTFT scaling with context length in multi-turn sessions.

Limitations

• Results are specific to tested hardware and software versions (vLLM 0.19.0, SGLang 0.5.9, PyTorch 2.10.0, CUDA 12.8).
• Distributional profiles approximate but do not replicate production traffic patterns.
• No consumer GPUs beyond RTX 3090; no non-NVIDIA accelerators.
• Closed-loop concurrency only; no open-loop (Poisson) arrivals.
• The model-hardware-concurrency matrix is sparse (12.2% fill for trace_replay, 3.0% for distributional). Not all model-hardware combinations are represented.
• No model quality metrics. This is a systems benchmark.

Ethical considerations

No PII. Trace-replay profiles derive from open benchmarks (SWE-Bench MIT, TerminalBench, OSWorld). Synthetic profiles use random tokens.

License

Benchmark data released under Apache-2.0. Source datasets retain their original licenses.

Source datasets

• SWE-Bench (MIT)
• TerminalBench
• ShareGPT (Aeala/ShareGPT_Vicuna_unfiltered)
• OSWorld

Citation

@inproceedings{agentperfbench2026,
  title={AgentPerfBench: A Benchmarking and Evaluation Suite for Inference Performance of Agentic LLMs},
  author={Anonymous},
  booktitle={NeurIPS 2026 Evaluations and Datasets Track},
  year={2026}
}