Datasets:
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: 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
- config_name: layer_roofline
data_files:
- split: summary
path: layer_roofline/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_examples: 3147
num_bytes: 694254
- 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: 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_examples: 245
num_bytes: 70836
- 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: validation_id
dtype: string
- name: tier
dtype: string
- name: stage
dtype: string
- name: stage_label
dtype: string
- name: comparison_group
dtype: string
- name: raw_json_r2_uri
dtype: string
- name: per_turn_json_r2_uri
dtype: string
- name: replay_kind
dtype: string
- name: workload
dtype: string
- name: profile
dtype: string
- name: model
dtype: string
- name: hardware
dtype: string
- name: engine
dtype: string
- name: tensor_parallelism
dtype: int64
- name: concurrency
dtype: int64
- name: sessions
dtype: int64
- name: source_locked
dtype: bool
- name: prefix_caching_state
dtype: string
- name: chunked_prefill
dtype: string
- name: prefix_aware_synthetic
dtype: bool
- name: shared_prefix_tokens
dtype: int64
- name: shared_prefix_block_aligned
dtype: bool
- name: synthetic_filler_style
dtype: string
- name: synthetic_target_chars_per_token
dtype: float64
- name: max_model_len
dtype: int64
- name: success_rate
dtype: float64
- 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_examples: 28
num_bytes: 40940
- config_name: layer_roofline
features:
- name: record_type
dtype: string
- name: source_file
dtype: string
- name: model
dtype: string
- name: hardware
dtype: string
- name: engine
dtype: string
- name: dtype
dtype: string
- name: tensor_parallelism
dtype: int64
- name: phase
dtype: string
- name: batch_size
dtype: int64
- name: sequence_length
dtype: int64
- name: q_len
dtype: int64
- name: kv_len
dtype: int64
- name: component_name
dtype: string
- name: component_bound
dtype: string
- name: flops
dtype: float64
- name: bytes
dtype: float64
- name: operational_intensity_flop_per_byte
dtype: float64
- name: ridge_point_flop_per_byte
dtype: float64
- name: total_flops
dtype: float64
- name: total_bytes
dtype: float64
- name: overall_bound
dtype: string
- 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
- name: artifact_path
dtype: string
- name: artifact_kind
dtype: string
- name: artifact_bytes
dtype: int64
- name: artifact_description
dtype: string
- name: notes
dtype: string
splits:
- name: summary
num_examples: 56
num_bytes: 27430
AgentPerfBench
LLM inference benchmark: 3,392 main sweep rows 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. The dataset also includes 148,077 per-kernel NCU profiles, 28 curated tabular MSE validation rows for the distributional synthetic replay generator, and 56 layer-roofline validation rows.
Dataset configurations
The dataset provides five configurations. trace_replay replays exact input/output sequences from recorded agent sessions. synthetic_distributional samples from statistical distributions fitted to those same workloads, trading fidelity for faster sweeps across the hardware matrix. kernels_labeled contains per-kernel Nsight Compute labels. mse_validation contains tabular paired synthetic-vs-real validation rows and ablations for the final APC-aware synthetic generator, with raw JSON artifacts referenced in R2 rather than stored in the dataset repo. layer_roofline exposes the per-layer roofline validation artifacts as a loadable tabular subset.
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
synthetic_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
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. The main rows keep paired MSE distributional replay and real trace replay runs with success rate at least 75%; supplementary rows preserve no-replacement and high-concurrency debug runs. Raw aggregate/per-turn JSON artifacts are referenced through R2 URI columns, not stored as dataset files.
The headline SWE-bench C=5 source-locked cascade is:
| Condition | Turn 10-19 E2EL delta |
|---|---|
| APC on, English filler, source-locked | +45.5% |
| APC on, code-morph filler, no shared prefix | +31.1% |
| APC off, code-morph filler, no shared prefix | +11.7% |
| APC on, code-morph filler, 1024-token shared prefix | -3.2% |
See mse_validation/README.md, mse_validation/manifest.csv, and mse_validation/fidelity_deltas.csv for the filtering rule and R2 raw-artifact URIs.
layer_roofline (56 rows)
Tabular view over the per-layer roofline evidence for Llama-3.1-8B on H100. This subset combines analytical component OI rows, selected NCU kernel summary rows, and an artifact manifest pointing to the raw per_layer_oi_cf/ evidence files.
Record types: analytical_total, analytical_component, ncu_kernel, artifact.
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}.
- synthetic_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 |
| synthetic_distributional | 245 |
| mse_validation | 28 |
| layer_roofline | 56 |
| kernels_labeled | 148,077 |
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 synthetic_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 the serving summary.parquet 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 "synthetic_distributional", "kernels_labeled", "mse_validation", "layer_roofline"
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} (synthetic_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
- Full per-request and multi-turn granularity data for the main sweep (pending raw JSON availability from collection infrastructure). Curated raw JSONs are referenced from R2 for
mse_validation. - Additional per-kernel roofline profiles beyond the included
kernels_labeled,layer_roofline, and rawper_layer_oi_cfartifacts. - 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).
- Synthetic distributional profiles approximate but do not replicate production traffic patterns.
- Consumer GPU coverage is limited to RTX 3090 and RTX 2080 Ti; 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 synthetic_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
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}
}