| --- |
| license: apache-2.0 |
| task_categories: |
| - tabular-regression |
| language: |
| - en |
| tags: |
| - llm-inference |
| - benchmarking |
| - gpu-profiling |
| - vllm |
| - sglang |
| - agentic-workloads |
| size_categories: |
| - 100K<n<1M |
| pretty_name: AgentPerfBench |
| version: "1.0" |
| configs: |
| - config_name: trace_replay |
| data_files: |
| - split: summary |
| path: trace_replay/summary.parquet |
| - config_name: synthetic_distributional |
| data_files: |
| - split: summary |
| path: synthetic_distributional/summary.parquet |
| - config_name: per_layer_kernel |
| data_files: |
| - split: summary |
| path: per_layer_kernel/summary.parquet |
| - config_name: kernels_labeled |
| data_files: |
| - split: train |
| path: kernel_profiles/kernels_labeled.parquet |
| - config_name: mse_validation |
| data_files: |
| - split: summary |
| path: mse_validation/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: 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_examples: 2932 |
| num_bytes: 640182 |
| - config_name: synthetic_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: 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_examples: 265 |
| - config_name: per_layer_kernel |
| features: |
| - name: record_type |
| dtype: string |
| - name: model |
| dtype: string |
| - name: hardware |
| dtype: string |
| - name: phase |
| dtype: string |
| - name: batch_size |
| dtype: int64 |
| - name: sequence_length |
| dtype: int64 |
| - name: component_name |
| dtype: string |
| - name: bound |
| dtype: string |
| - name: flops |
| dtype: float64 |
| - name: bytes_accessed |
| dtype: float64 |
| - name: operational_intensity |
| dtype: float64 |
| - name: ridge_point |
| dtype: float64 |
| - name: kernel_id |
| dtype: int64 |
| - name: kernel_name |
| dtype: string |
| - name: block_size |
| dtype: string |
| - name: grid_size |
| dtype: string |
| - name: duration_us |
| dtype: float64 |
| - name: compute_sm_throughput_pct |
| dtype: float64 |
| - name: dram_throughput_pct |
| dtype: float64 |
| - name: memory_throughput_pct |
| dtype: float64 |
| - name: l1_tex_cache_throughput_pct |
| dtype: float64 |
| - name: l2_cache_throughput_pct |
| dtype: float64 |
| - name: sm_frequency_ghz |
| dtype: float64 |
| - name: dram_frequency_ghz |
| dtype: float64 |
| splits: |
| - name: summary |
| num_examples: 37 |
| num_bytes: 12000 |
| - config_name: kernels_labeled |
| features: |
| - name: source |
| dtype: string |
| - name: gpu |
| dtype: string |
| - name: model |
| dtype: string |
| - name: kernel_family |
| dtype: string |
| - name: kernel_name |
| dtype: string |
| - name: dtype |
| dtype: string |
| - name: held_out |
| dtype: bool |
| - name: M |
| dtype: float64 |
| - name: N |
| dtype: float64 |
| - name: K |
| dtype: float64 |
| - name: bs |
| dtype: float64 |
| - name: seq |
| dtype: float64 |
| - name: n_heads |
| dtype: float64 |
| - name: head_dim |
| dtype: float64 |
| - name: kv_heads |
| dtype: float64 |
| - name: numel |
| dtype: float64 |
| - name: op_type |
| dtype: string |
| - name: gpu_time_duration_ms |
| dtype: float64 |
| - name: launch_block_size |
| dtype: float64 |
| - name: launch_grid_size |
| dtype: float64 |
| - name: dram_bytes_sum |
| dtype: float64 |
| - name: launch_registers_per_thread |
| dtype: float64 |
| splits: |
| - name: train |
| num_examples: 148077 |
| - config_name: mse_validation |
| features: |
| - name: run_id |
| dtype: string |
| - name: model |
| dtype: string |
| - name: hardware |
| dtype: string |
| - name: engine |
| dtype: string |
| - name: profile |
| dtype: string |
| - name: concurrency |
| dtype: int64 |
| - name: num_requests |
| dtype: int64 |
| - name: successful_requests |
| dtype: int64 |
| - name: failed_requests |
| dtype: int64 |
| - name: duration_s |
| dtype: float64 |
| - name: request_throughput |
| dtype: float64 |
| - name: mean_ttft_ms |
| dtype: float64 |
| - name: mean_tpot_ms |
| dtype: float64 |
| - name: mean_e2el_ms |
| dtype: float64 |
| splits: |
| - name: summary |
| num_examples: 28 |
| --- |
| |
| # AgentPerfBench |
|
|
| LLM inference benchmark: 3,197 main sweep rows and 37 per-layer kernel validation rows, plus 148,077 per-kernel NCU profiles, across 9 models, 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 |
|
|
| ### trace_replay (2,932 rows) |
| |
| Replays exact ISL/OSL sequences from recorded agent sessions (SWE-Bench, TerminalBench, OSWorld, ShareGPT). 77 unique (model, hardware, engine) combinations across 17 profiles. |
| |
| 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` |
| |
| ### synthetic_distributional (265 rows) |
|
|
| ISL/OSL sampled from lognormal fits to real workload statistics. 38 unique (model, hardware, engine) combinations across 5 profiles. |
|
|
| 5 profiles: `chat-multiturn-synth`, `chat-singleturn-synth`, `osworld-multiturn-synth`, `swebench-multiturn-synth`, `terminalbench-multiturn-synth` |
|
|
| ### per_layer_kernel (37 rows) |
|
|
| Per-component operational intensity decomposition and Nsight Compute kernel profiles for Llama-3.1-8B on H100 (prefill phase). Analytical rows provide computed FLOPs, bytes, and OI per model component at batch sizes 1 and 80. NCU rows report measured SM and memory throughput per kernel from an 8-layer forward pass. Record types: `analytical_total`, `analytical_component`, `ncu_kernel`. |
|
|
| ### kernels_labeled (148,077 rows) |
| |
| Per-kernel Nsight Compute (ncu) profiles across 4 GPUs (A100, H100, RTX 3090, RTX 2080 Ti) and 13 model/sweep sources. |
| |
| ### mse_validation (28 rows) |
|
|
| Curated H100 / Llama-3.1-8B / vLLM validation table for the distributional synthetic replay generator. Paired synthetic and real trace replay runs; supplementary rows preserve no-replacement and high-concurrency debug runs. Raw JSON artifacts referenced through R2 URI columns. Per-run successful/failed request counts retained. |
|
|
| ### Quality filtering |
|
|
| Configurations where fewer than 75% of requests completed successfully are excluded. Summary metrics are computed from successful requests only. |
|
|
| | Config | Rows | |
| |--------|------| |
| | trace_replay | 2,932 | |
| | synthetic_distributional | 265 | |
| | per_layer_kernel | 37 | |
| | kernels_labeled | 148,077 | |
| | mse_validation | 28 | |
|
|
| ## Coverage |
|
|
| ### Hardware |
|
|
| All benchmarks collected on PyTorch 2.10.0. |
|
|
| | 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. |
|
|
| ### 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 | |
|
|
| ### Engines |
|
|
| - vLLM 0.19.0 |
| - SGLang 0.5.9 |
|
|
| ## Schema |
|
|
| Each row in `summary.parquet` (trace_replay and synthetic_distributional): |
|
|
| | 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 | |
| | 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 |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("agent-perf-bench/AgentPerfBench", "trace_replay") |
| # or "synthetic_distributional", "per_layer_kernel", "kernels_labeled", "mse_validation" |
| ``` |
|
|
| ## Benchmark methodology |
|
|
| - Closed-loop concurrency with semaphore control. |
| - 3-request warmup before each configuration. |
| - Metrics: TTFT, TPOT, ITL, E2EL, request throughput, token throughput (mean, median, p90, p99). |
| - Metrics computed over successful requests only. |
| - Collection period: March 2026 onwards. |
|
|
| ## Limitations |
|
|
| - Distributional profiles are fitted approximations, not direct production replays. |
| - Closed-loop concurrency only; no open-loop (Poisson) arrivals. |
|
|
| ## 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](https://github.com/princeton-nlp/SWE-bench) (MIT) |
| - [TerminalBench](https://github.com/TerminalBench/TerminalBench) |
| - [ShareGPT (Aeala/ShareGPT_Vicuna_unfiltered)](https://huggingface.co/datasets/Aeala/ShareGPT_Vicuna_unfiltered) |
| - [OSWorld](https://github.com/xlang-ai/OSWorld) |
|
|
| ## Future releases |
|
|
| - Additional hardware configurations and model families. |
| - Open-loop (Poisson) arrival mode. |
| - Additional per-kernel roofline profiles. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
