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README.md

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+---
+license: apache-2.0
+task_categories:
+  - tabular-regression
+language:
+  - en
+tags:
+  - llm-inference
+  - benchmarking
+  - gpu-profiling
+  - vllm
+  - sglang
+  - roofline
+  - agentic-workloads
+size_categories:
+  - 1K<n<10K
+pretty_name: AgentPerfBench
+configs:
+  - config_name: benchmark_results
+    data_files:
+      - split: summary
+        path: benchmark_results/summary.parquet
+      - split: per_request
+        path: benchmark_results/per_request.parquet
+      - split: multi_turn
+        path: benchmark_results/multi_turn.parquet
+  - config_name: roofline
+    data_files:
+      - split: kernel_profiles
+        path: roofline/kernel_profiles.parquet
+---
+
+# AgentPerfBench
+
+## Dataset Summary
+
+AgentPerfBench measures LLM inference serving performance under agentic workloads. The dataset contains benchmark results (TTFT, TPOT, ITL, throughput) across 9 models, 3 GPU platforms, 2 serving engines, and 14+ workload profiles spanning single-turn chat, multi-turn agent sessions, and synthetic stress tests. It also includes per-kernel CUDA profiling data for roofline analysis. Data was collected in April 2026.
+
+## Data Description
+
+### Parquet Files
+
+| File | Description | Rows (approx.) |
+|------|-------------|-----------------|
+| `benchmark_results/summary.parquet` | One row per benchmark configuration (model x hardware x engine x profile x concurrency). Columns: `run_id`, `model`, `hardware`, `engine`, `tensor_parallelism`, `profile`, `concurrency`, plus TTFT/TPOT/ITL/E2EL percentiles and throughput metrics. | 2500-3500 |
+| `benchmark_results/per_request.parquet` | Per-request latency data. FK: `run_id` references `summary`. | 150K-200K |
+| `benchmark_results/multi_turn.parquet` | Per-turn breakdowns for multi-turn sessions. FK: `run_id` references `summary`. | varies |
+| `roofline/kernel_profiles.parquet` | Per-kernel CUDA profiling data from Nsight Compute. One row per kernel invocation. | varies |
+
+### Metadata JSONs
+
+- `metadata/models.json` — model specifications (family, parameters, architecture, license)
+- `metadata/hardware.json` — GPU specs (VRAM, bandwidth, peak TFLOPS)
+- `metadata/profiles.json` — workload profile definitions (ISL/OSL ranges, source, tier)
+
+## Benchmark Methodology
+
+- **Concurrency model**: Closed-loop with semaphore control. One request admitted per completion.
+- **Concurrency sweep**: 1 to 320.
+- **Requests per configuration**: 50, with 3-request warmup.
+- **Metrics**: TTFT, TPOT (time per output token), ITL (inter-token latency), E2EL (end-to-end latency), request throughput (req/s), token throughput (tok/s).
+- **Percentiles**: mean, median, p90, p99.
+- **Kernel profiling**: PyTorch profiler on 2-layer forward passes, batch sizes [1, 4, 8, 32, 64].
+
+## Models
+
+| Model | Family | Parameters | Architecture | License |
+|-------|--------|-----------|--------------|---------|
+| Llama-3.1-8B-Instruct | Llama | 8B | Dense | Llama 3.1 Community |
+| Llama-3.1-70B-Instruct | Llama | 70B | Dense | Llama 3.1 Community |
+| Llama-3.3-70B-Instruct | Llama | 70B | Dense | Llama 3.3 Community |
+| Qwen2.5-72B-Instruct | Qwen | 72B | Dense | Apache 2.0 |
+| Qwen3.5-9B | Qwen | 9B | Dense | Apache 2.0 |
+| Qwen3.5-27B | Qwen | 27B | Dense | Apache 2.0 |
+| gpt-oss-20b | GPT-OSS | 21B (3.6B active) | MoE (32 experts, top-4) | Apache 2.0 |
+| gpt-oss-120b | GPT-OSS | 117B (5.1B active) | MoE (128 experts, top-4) | Apache 2.0 |
+
+## Hardware
+
+| GPU | VRAM | HBM Bandwidth | Peak BF16 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 |
+
+## Workload Profiles
+
+| Profile | Tier | ISL | OSL | Source | Synthetic? |
+|---------|------|-----|-----|--------|------------|
+| chat-short | 2 (Chat) | 500 | 300 | ShareGPT | No |
+| chat-medium | 2 (Chat) | 2000 | 1000 | ShareGPT | No |
+| chat-long | 2 (Chat) | 8000 | 2000 | ShareGPT | No |
+| coding-agent | 1 (Agentic) | 17000 | 800 | SWE-Bench | No |
+| chat-multiturn-short | 2 (Chat MT) | 8192 | 1000 | ShareGPT | No |
+| chat-multiturn-medium | 2 (Chat MT) | 16384 | 1500 | ShareGPT | No |
+| chat-multiturn-long | 2 (Chat MT) | 32768 | 2000 | ShareGPT | No |
+| swebench-multiturn-short | 1 (Agentic MT) | 32768 | 2000 | SWE-Bench | No |
+| swebench-multiturn-medium | 1 (Agentic MT) | 65536 | 2000 | SWE-Bench | No |
+| terminalbench-multiturn-short | 1 (Agentic MT) | 32768 | 2000 | TerminalBench | No |
+| terminalbench-multiturn-medium | 1 (Agentic MT) | 65536 | 2000 | TerminalBench | No |
+| decode-heavy | 3 (Synthetic) | 256 | 4096 | Random | Yes |
+| prefill-heavy | 3 (Synthetic) | 8192 | 256 | Random | Yes |
+| random-1k | 3 (Synthetic) | 1024 | 1024 | Random | Yes |
+
+## Intended Uses
+
+- Comparing inference engine performance under controlled conditions.
+- Capacity planning for agentic LLM deployments.
+- Roofline analysis of GPU utilization under different workload regimes.
+- Studying TTFT degradation under multi-turn context accumulation.
+
+## Limitations
+
+- Results are specific to tested hardware and software versions (vLLM 0.19.0, SGLang 0.5.9).
+- Synthetic profiles approximate but do not replicate production traffic patterns.
+- No consumer GPUs beyond RTX 3090; no non-NVIDIA accelerators.
+- Closed-loop concurrency only; open-loop (Poisson arrival) not included.
+- No model quality metrics. This is a systems benchmark.
+
+## Ethical Considerations
+
+- No PII in the dataset.
+- Synthetic profiles use random tokens. Real-trace profiles derive from open benchmarks (SWE-Bench MIT, TerminalBench).
+- Benchmark results should not be used as sole basis for hardware purchasing decisions.
+
+## Source Datasets
+
+- [SWE-Bench](https://github.com/princeton-nlp/SWE-bench) (MIT License) — coding agent traces
+- [TerminalBench](https://github.com/TerminalBench/TerminalBench) — terminal agent traces
+- [ShareGPT (Aeala/ShareGPT_Vicuna_unfiltered)](https://huggingface.co/datasets/Aeala/ShareGPT_Vicuna_unfiltered) — chat baseline prompts (used as input length distribution, not redistributed)
+- [OSWorld](https://github.com/xlang-ai/OSWorld) — computer-use agent traces
+
+## 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}
+}
+```

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+  ],
+  "rai:dataLimitations": "Results cover NVIDIA H100, A100, and RTX 3090 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 datacenter GPUs. Workload profiles are author-designed approximations of production traffic; real deployment patterns may differ. ShareGPT chat baselines reflect English-language conversations only.",
+  "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. Raw user conversations from ShareGPT are not included in this dataset.",
+  "rai:dataUseCases": "Established uses: relative comparison of inference engine throughput, latency benchmarking under controlled conditions, GPU roofline analysis, studying TTFT degradation under multi-turn context growth. 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. Could be misused for misleading marketing claims about model or hardware performance without proper context.",
+  "rai:hasSyntheticData": true,
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+      "@id": "https://huggingface.co/datasets/princeton-nlp/SWE-bench",
+      "name": "SWE-Bench"
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+      "name": "TerminalBench"
+    }
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+  }
+}

metadata/hardware.json

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+  {"name": "RTX_3090_24GB", "gpu": "NVIDIA RTX 3090", "vram_gb": 24, "hbm_bandwidth_tb_s": 0.936, "peak_tflops_bf16": 71, "interconnect": "PCIe"}
+]

metadata/models.json

@@ -0,0 +1,11 @@
+[
+  {"name": "Llama-3.1-8B-Instruct", "family": "llama", "params_b": 8, "architecture": "dense", "license": "llama3.1"},
+  {"name": "Llama-3.1-70B-Instruct", "family": "llama", "params_b": 70, "architecture": "dense", "license": "llama3.1"},
+  {"name": "Llama-3.3-70B-Instruct", "family": "llama", "params_b": 70, "architecture": "dense", "license": "llama3.3"},
+  {"name": "Qwen2.5-72B-Instruct", "family": "qwen", "params_b": 72, "architecture": "dense", "license": "apache-2.0"},
+  {"name": "Qwen3.5-9B", "family": "qwen", "params_b": 9, "architecture": "dense", "license": "apache-2.0"},
+  {"name": "Qwen3.5-27B", "family": "qwen", "params_b": 27, "architecture": "dense", "license": "apache-2.0"},
+  {"name": "Mixtral-8x7B-Instruct-v0.1", "family": "mixtral", "params_b": 47, "active_params_b": 13, "architecture": "moe", "num_experts": 8, "top_k": 2, "license": "apache-2.0"},
+  {"name": "gpt-oss-20b", "family": "gpt-oss", "params_b": 21, "active_params_b": 3.6, "architecture": "moe", "num_experts": 32, "top_k": 4, "license": "apache-2.0"},
+  {"name": "gpt-oss-120b", "family": "gpt-oss", "params_b": 117, "active_params_b": 5.1, "architecture": "moe", "num_experts": 128, "top_k": 4, "license": "apache-2.0"}
+]

metadata/profiles.json

@@ -0,0 +1,24 @@
+[
+  {"name": "coding-agent", "tier": 1, "description": "Coding-agent prompts from SWE-Bench runs (planning calls)", "isl_range": [17000, 17000], "osl_range": [800, 800], "source": "swebench", "is_synthetic": false, "is_multi_turn": false},
+  {"name": "swebench-multiturn-short", "tier": 1, "description": "SWE-Bench coding agent: 13-30 step sessions", "isl_range": [32768, 32768], "osl_range": [2000, 2000], "source": "swebench", "is_synthetic": false, "is_multi_turn": true},
+  {"name": "swebench-multiturn-medium", "tier": 1, "description": "SWE-Bench coding agent: 30-80 step sessions", "isl_range": [65536, 65536], "osl_range": [2000, 2000], "source": "swebench", "is_synthetic": false, "is_multi_turn": true},
+  {"name": "swebench-multiturn-long", "tier": 1, "description": "SWE-Bench coding agent: 80-150 step sessions", "isl_range": [131072, 131072], "osl_range": [2000, 2000], "source": "swebench", "is_synthetic": false, "is_multi_turn": true},
+  {"name": "swebench-multiturn-xl", "tier": 1, "description": "SWE-Bench coding agent: 150+ step sessions", "isl_range": [131072, 131072], "osl_range": [2000, 2000], "source": "swebench", "is_synthetic": false, "is_multi_turn": true},
+  {"name": "terminalbench-multiturn-short", "tier": 1, "description": "TerminalBench CLI agent: 2-20 step sessions", "isl_range": [32768, 32768], "osl_range": [2000, 2000], "source": "terminalbench", "is_synthetic": false, "is_multi_turn": true},
+  {"name": "terminalbench-multiturn-medium", "tier": 1, "description": "TerminalBench CLI agent: 20-60 step sessions", "isl_range": [65536, 65536], "osl_range": [2000, 2000], "source": "terminalbench", "is_synthetic": false, "is_multi_turn": true},
+  {"name": "terminalbench-multiturn-long", "tier": 1, "description": "TerminalBench CLI agent: 60-150 step sessions", "isl_range": [131072, 131072], "osl_range": [2000, 2000], "source": "terminalbench", "is_synthetic": false, "is_multi_turn": true},
+  {"name": "terminalbench-multiturn-xl", "tier": 1, "description": "TerminalBench CLI agent: 150+ step sessions", "isl_range": [131072, 131072], "osl_range": [2000, 2000], "source": "terminalbench", "is_synthetic": false, "is_multi_turn": true},
+  {"name": "osworld-multiturn-short", "tier": 1, "description": "OSWorld computer-use agent: 2-10 step sessions", "isl_range": [32768, 32768], "osl_range": [500, 500], "source": "osworld", "is_synthetic": false, "is_multi_turn": true},
+  {"name": "osworld-multiturn-medium", "tier": 1, "description": "OSWorld computer-use agent: 10-20 step sessions", "isl_range": [65536, 65536], "osl_range": [500, 500], "source": "osworld", "is_synthetic": false, "is_multi_turn": true},
+  {"name": "osworld-multiturn-long", "tier": 1, "description": "OSWorld computer-use agent: 20-30 step sessions", "isl_range": [131072, 131072], "osl_range": [500, 500], "source": "osworld", "is_synthetic": false, "is_multi_turn": true},
+  {"name": "chat-short", "tier": 2, "description": "Short Q&A chat from ShareGPT", "isl_range": [100, 500], "osl_range": [50, 300], "source": "sharegpt", "is_synthetic": false, "is_multi_turn": false},
+  {"name": "chat-medium", "tier": 2, "description": "Medium-length ShareGPT conversations", "isl_range": [500, 2000], "osl_range": [300, 1000], "source": "sharegpt", "is_synthetic": false, "is_multi_turn": false},
+  {"name": "chat-long", "tier": 2, "description": "Long ShareGPT conversations", "isl_range": [2000, 8000], "osl_range": [1000, 2000], "source": "sharegpt", "is_synthetic": false, "is_multi_turn": false},
+  {"name": "chat-multiturn-short", "tier": 2, "description": "ShareGPT multi-turn chat: 3-5 turns", "isl_range": [1000, 8192], "osl_range": [200, 1000], "source": "sharegpt", "is_synthetic": false, "is_multi_turn": true},
+  {"name": "chat-multiturn-medium", "tier": 2, "description": "ShareGPT multi-turn chat: 5-10 turns", "isl_range": [2000, 16384], "osl_range": [500, 1500], "source": "sharegpt", "is_synthetic": false, "is_multi_turn": true},
+  {"name": "chat-multiturn-long", "tier": 2, "description": "ShareGPT multi-turn chat: 10-20 turns", "isl_range": [4000, 32768], "osl_range": [1000, 2000], "source": "sharegpt", "is_synthetic": false, "is_multi_turn": true},
+  {"name": "chat-multiturn-xl", "tier": 2, "description": "ShareGPT multi-turn chat: 20-30 turns", "isl_range": [8000, 65536], "osl_range": [1000, 2000], "source": "sharegpt", "is_synthetic": false, "is_multi_turn": true},
+  {"name": "prefill-heavy", "tier": 3, "description": "Synthetic prefill stress: long input, short output", "isl_range": [8192, 8192], "osl_range": [256, 256], "source": "random", "is_synthetic": true, "is_multi_turn": false},
+  {"name": "decode-heavy", "tier": 3, "description": "Synthetic decode stress: short input, long output", "isl_range": [256, 256], "osl_range": [4096, 4096], "source": "random", "is_synthetic": true, "is_multi_turn": false},
+  {"name": "random-1k", "tier": 3, "description": "Cross-validation baseline: balanced random tokens", "isl_range": [1024, 1024], "osl_range": [1024, 1024], "source": "random", "is_synthetic": true, "is_multi_turn": false}
+]

roofline/kernel_profiles.parquet

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