Upload folder using huggingface_hub

README.md

@@ -1,366 +1,358 @@
----
-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: distributional
-    data_files:
-      - split: summary
-        path: distributional/summary.parquet
-  - config_name: kernels_labeled
-    data_files:
-      - split: train
-        path: kernel_profiles/kernels_labeled.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: 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
----
-
-# AgentPerfBench
-
-LLM inference benchmark: 3,392 serving runs and 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 (3,147 rows)
-
-Replays exact ISL/OSL sequences from recorded agent sessions (SWE-Bench, TerminalBench, OSWorld, ShareGPT). 77 (model, hardware, engine) combinations, 17 profiles, 6 concurrency levels.
-
-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)
-
-ISL/OSL sampled from lognormal fits to real workload statistics. 42 combinations, 6 profiles, 7 concurrency levels.
-
-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.
-
-### Concurrency filtering
-
-Concurrency is controlled by a fixed-size connection pool. Trace replay uses levels {1, 5, 10, 20, 40, 80}; distributional uses {1, 5, 10, 40, 80, 200, 320}.
-
-Early runs used a session-pool size smaller than the declared concurrency (`num_sessions=100` for trace replay, `num_sessions=10` for distributional), capping actual load below the nominal value. Rows where declared concurrency exceeded the session pool were dropped.
-
-| Config | Rows |
-|--------|------|
-| trace_replay | 3,147 |
-| distributional | 245 |
-| **Total** | **3,392** |
-
-## 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.
-
-### 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` (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
-
-```python
-from datasets import load_dataset
-
-ds = load_dataset("agent-perf-bench/AgentPerfBench", "trace_replay")
-# or "distributional", "kernels_labeled"
-```
-
-## 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)
-
-## 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}
-}
-```
+---
+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: distributional
+    data_files:
+      - split: summary
+        path: distributional/summary.parquet
+  - config_name: kernels_labeled
+    data_files:
+      - split: train
+        path: kernel_profiles/kernels_labeled.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: 2902
+        num_bytes: 640182
+  - 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: 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
+---
+
+# AgentPerfBench
+
+LLM inference benchmark: 3,147 serving runs and 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 (2,902 rows)
+
+Replays exact ISL/OSL sequences from recorded agent sessions (SWE-Bench, TerminalBench, OSWorld, ShareGPT). 77 (model, hardware, engine) combinations, 17 profiles, 6 concurrency levels.
+
+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)
+
+ISL/OSL sampled from lognormal fits to real workload statistics. 42 combinations, 6 profiles, 7 concurrency levels.
+
+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.
+
+### Concurrency filtering
+
+Concurrency is controlled by a fixed-size connection pool. Trace replay uses levels {1, 5, 10, 20, 40, 80}; distributional uses {1, 5, 10, 40, 80, 200, 320}.
+
+Early runs used a session-pool size smaller than the declared concurrency (`num_sessions=100` for trace replay, `num_sessions=10` for distributional), capping actual load below the nominal value. Rows where declared concurrency exceeded the session pool were dropped.
+
+Configurations where fewer than 75% of requests completed successfully are excluded.
+
+| Config | Rows |
+|--------|------|
+| trace_replay | 2,902 |
+| distributional | 245 |
+| **Total** | **3,147** |
+
+## 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` (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 |
+| 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 "distributional", "kernels_labeled"
+```
+
+## 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)
+
+## 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}
+}
+```

croissant.json

@@ -1,501 +1,501 @@
-{
-  "@context": {
-    "@language": "en",
-    "@vocab": "https://schema.org/",
-    "citeAs": "cr:citeAs",
-    "column": "cr:column",
-    "conformsTo": "dct:conformsTo",
-    "cr": "http://mlcommons.org/croissant/",
-    "data": {
-      "@id": "cr:data",
-      "@type": "@json"
-    },
-    "dataType": {
-      "@id": "cr:dataType",
-      "@type": "@vocab"
-    },
-    "dct": "http://purl.org/dc/terms/",
-    "equivalentProperty": "cr:equivalentProperty",
-    "examples": {
-      "@id": "cr:examples",
-      "@type": "@json"
-    },
-    "extract": "cr:extract",
-    "field": "cr:field",
-    "fileObject": "cr:fileObject",
-    "fileProperty": "cr:fileProperty",
-    "fileSet": "cr:fileSet",
-    "format": "cr:format",
-    "includes": "cr:includes",
-    "isLiveDataset": "cr:isLiveDataset",
-    "jsonPath": "cr:jsonPath",
-    "key": "cr:key",
-    "md5": "cr:md5",
-    "parentField": "cr:parentField",
-    "path": "cr:path",
-    "rai": "http://mlcommons.org/croissant/RAI/",
-    "recordSet": "cr:recordSet",
-    "references": "cr:references",
-    "regex": "cr:regex",
-    "repeated": "cr:repeated",
-    "replace": "cr:replace",
-    "samplingRate": "cr:samplingRate",
-    "prov": "http://www.w3.org/ns/prov#",
-    "sc": "https://schema.org/",
-    "separator": "cr:separator",
-    "source": "cr:source",
-    "subField": "cr:subField",
-    "transform": "cr:transform"
-  },
-  "@type": "sc:Dataset",
-  "name": "AgentPerfBench",
-  "description": "LLM inference benchmark dataset measuring serving performance (TTFT, TPOT, throughput) across 9 models, 4 GPU platforms, 2 serving engines under agentic and chat workloads. Three configurations: trace_replay, distributional, and kernels_labeled.",
-  "url": "https://huggingface.co/datasets/agent-perf-bench/AgentPerfBench",
-  "license": "https://spdx.org/licenses/Apache-2.0.html",
-  "conformsTo": "http://mlcommons.org/croissant/1.1",
-  "datePublished": "2026-05-04",
-  "version": "1.0",
-  "citeAs": "@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}}",
-  "creator": {
-    "@type": "sc:Organization",
-    "name": "Anonymous (under review)"
-  },
-  "distribution": [
-    {
-      "@type": "cr:FileObject",
-      "@id": "trace-replay-summary-parquet",
-      "name": "trace_replay/summary.parquet",
-      "contentUrl": "https://huggingface.co/datasets/agent-perf-bench/AgentPerfBench/resolve/main/trace_replay/summary.parquet",
-      "encodingFormat": "application/x-parquet",
-      "sha256": "a9cd2565a9292e75f54791e8108f2491b7bb371fdfeeefb81cdb00833860dd23"
-    },
-    {
-      "@type": "cr:FileObject",
-      "@id": "distributional-summary-parquet",
-      "name": "distributional/summary.parquet",
-      "contentUrl": "https://huggingface.co/datasets/agent-perf-bench/AgentPerfBench/resolve/main/distributional/summary.parquet",
-      "encodingFormat": "application/x-parquet",
-      "sha256": "bb9a48351e0b612b3125c5d0a8e200638d790269f821e45aae342a4042bfd951"
-    },
-    {
-      "@type": "cr:FileObject",
-      "@id": "kernels-labeled-parquet",
-      "name": "kernel_profiles/kernels_labeled.parquet",
-      "contentUrl": "https://huggingface.co/datasets/agent-perf-bench/AgentPerfBench/resolve/main/kernel_profiles/kernels_labeled.parquet",
-      "encodingFormat": "application/x-parquet",
-      "sha256": "05571ee61fe97beac7f3d0b0e8be07a0feeca00cee9b308cd469a57cd4c9b671"
-    }
-  ],
-  "recordSet": [
-    {
-      "@type": "cr:RecordSet",
-      "@id": "trace-replay-summary",
-      "name": "Trace Replay Summary",
-      "description": "One row per benchmark configuration from trace replay runs (empirical agent session replays).",
-      "field": [
-        {
-          "@type": "cr:Field",
-          "@id": "trace-replay-summary/run_id",
-          "name": "run_id",
-          "dataType": "sc:Text",
-          "source": {
-            "fileObject": {
-              "@id": "trace-replay-summary-parquet"
-            },
-            "extract": {
-              "column": "run_id"
-            }
-          }
-        },
-        {
-          "@type": "cr:Field",
-          "@id": "trace-replay-summary/model",
-          "name": "model",
-          "dataType": "sc:Text",
-          "source": {
-            "fileObject": {
-              "@id": "trace-replay-summary-parquet"
-            },
-            "extract": {
-              "column": "model"
-            }
-          }
-        },
-        {
-          "@type": "cr:Field",
-          "@id": "trace-replay-summary/hardware",
-          "name": "hardware",
-          "dataType": "sc:Text",
-          "source": {
-            "fileObject": {
-              "@id": "trace-replay-summary-parquet"
-            },
-            "extract": {
-              "column": "hardware"
-            }
-          }
-        },
-        {
-          "@type": "cr:Field",
-          "@id": "trace-replay-summary/engine",
-          "name": "engine",
-          "dataType": "sc:Text",
-          "source": {
-            "fileObject": {
-              "@id": "trace-replay-summary-parquet"
-            },
-            "extract": {
-              "column": "engine"
-            }
-          }
-        },
-        {
-          "@type": "cr:Field",
-          "@id": "trace-replay-summary/profile",
-          "name": "profile",
-          "dataType": "sc:Text",
-          "source": {
-            "fileObject": {
-              "@id": "trace-replay-summary-parquet"
-            },
-            "extract": {
-              "column": "profile"
-            }
-          }
-        },
-        {
-          "@type": "cr:Field",
-          "@id": "trace-replay-summary/concurrency",
-          "name": "concurrency",
-          "dataType": "sc:Integer",
-          "source": {
-            "fileObject": {
-              "@id": "trace-replay-summary-parquet"
-            },
-            "extract": {
-              "column": "concurrency"
-            }
-          }
-        },
-        {
-          "@type": "cr:Field",
-          "@id": "trace-replay-summary/request_throughput",
-          "name": "request_throughput",
-          "dataType": "sc:Float",
-          "source": {
-            "fileObject": {
-              "@id": "trace-replay-summary-parquet"
-            },
-            "extract": {
-              "column": "request_throughput"
-            }
-          }
-        },
-        {
-          "@type": "cr:Field",
-          "@id": "trace-replay-summary/median_ttft_ms",
-          "name": "median_ttft_ms",
-          "dataType": "sc:Float",
-          "source": {
-            "fileObject": {
-              "@id": "trace-replay-summary-parquet"
-            },
-            "extract": {
-              "column": "median_ttft_ms"
-            }
-          }
-        },
-        {
-          "@type": "cr:Field",
-          "@id": "trace-replay-summary/median_tpot_ms",
-          "name": "median_tpot_ms",
-          "dataType": "sc:Float",
-          "source": {
-            "fileObject": {
-              "@id": "trace-replay-summary-parquet"
-            },
-            "extract": {
-              "column": "median_tpot_ms"
-            }
-          }
-        }
-      ]
-    },
-    {
-      "@type": "cr:RecordSet",
-      "@id": "distributional-summary",
-      "name": "Distributional Summary",
-      "description": "One row per benchmark configuration from distributional runs (statistical sampling from lognormal fits to recorded workload statistics).",
-      "field": [
-        {
-          "@type": "cr:Field",
-          "@id": "distributional-summary/run_id",
-          "name": "run_id",
-          "dataType": "sc:Text",
-          "source": {
-            "fileObject": {
-              "@id": "distributional-summary-parquet"
-            },
-            "extract": {
-              "column": "run_id"
-            }
-          }
-        },
-        {
-          "@type": "cr:Field",
-          "@id": "distributional-summary/model",
-          "name": "model",
-          "dataType": "sc:Text",
-          "source": {
-            "fileObject": {
-              "@id": "distributional-summary-parquet"
-            },
-            "extract": {
-              "column": "model"
-            }
-          }
-        },
-        {
-          "@type": "cr:Field",
-          "@id": "distributional-summary/hardware",
-          "name": "hardware",
-          "dataType": "sc:Text",
-          "source": {
-            "fileObject": {
-              "@id": "distributional-summary-parquet"
-            },
-            "extract": {
-              "column": "hardware"
-            }
-          }
-        },
-        {
-          "@type": "cr:Field",
-          "@id": "distributional-summary/engine",
-          "name": "engine",
-          "dataType": "sc:Text",
-          "source": {
-            "fileObject": {
-              "@id": "distributional-summary-parquet"
-            },
-            "extract": {
-              "column": "engine"
-            }
-          }
-        },
-        {
-          "@type": "cr:Field",
-          "@id": "distributional-summary/profile",
-          "name": "profile",
-          "dataType": "sc:Text",
-          "source": {
-            "fileObject": {
-              "@id": "distributional-summary-parquet"
-            },
-            "extract": {
-              "column": "profile"
-            }
-          }
-        },
-        {
-          "@type": "cr:Field",
-          "@id": "distributional-summary/concurrency",
-          "name": "concurrency",
-          "dataType": "sc:Integer",
-          "source": {
-            "fileObject": {
-              "@id": "distributional-summary-parquet"
-            },
-            "extract": {
-              "column": "concurrency"
-            }
-          }
-        },
-        {
-          "@type": "cr:Field",
-          "@id": "distributional-summary/request_throughput",
-          "name": "request_throughput",
-          "dataType": "sc:Float",
-          "source": {
-            "fileObject": {
-              "@id": "distributional-summary-parquet"
-            },
-            "extract": {
-              "column": "request_throughput"
-            }
-          }
-        },
-        {
-          "@type": "cr:Field",
-          "@id": "distributional-summary/median_ttft_ms",
-          "name": "median_ttft_ms",
-          "dataType": "sc:Float",
-          "source": {
-            "fileObject": {
-              "@id": "distributional-summary-parquet"
-            },
-            "extract": {
-              "column": "median_ttft_ms"
-            }
-          }
-        },
-        {
-          "@type": "cr:Field",
-          "@id": "distributional-summary/median_tpot_ms",
-          "name": "median_tpot_ms",
-          "dataType": "sc:Float",
-          "source": {
-            "fileObject": {
-              "@id": "distributional-summary-parquet"
-            },
-            "extract": {
-              "column": "median_tpot_ms"
-            }
-          }
-        }
-      ]
-    },
-    {
-      "@type": "cr:RecordSet",
-      "@id": "kernels-labeled",
-      "name": "Kernels Labeled",
-      "description": "Per-kernel Nsight Compute (ncu) hardware counter profiles. Individual kernel invocations across 4 GPUs and 13 model/sweep sources.",
-      "field": [
-        {
-          "@type": "cr:Field",
-          "@id": "kernels-labeled/source",
-          "name": "source",
-          "dataType": "sc:Text",
-          "source": {
-            "fileObject": {
-              "@id": "kernels-labeled-parquet"
-            },
-            "extract": {
-              "column": "source"
-            }
-          }
-        },
-        {
-          "@type": "cr:Field",
-          "@id": "kernels-labeled/gpu",
-          "name": "gpu",
-          "dataType": "sc:Text",
-          "source": {
-            "fileObject": {
-              "@id": "kernels-labeled-parquet"
-            },
-            "extract": {
-              "column": "gpu"
-            }
-          }
-        },
-        {
-          "@type": "cr:Field",
-          "@id": "kernels-labeled/model",
-          "name": "model",
-          "dataType": "sc:Text",
-          "source": {
-            "fileObject": {
-              "@id": "kernels-labeled-parquet"
-            },
-            "extract": {
-              "column": "model"
-            }
-          }
-        },
-        {
-          "@type": "cr:Field",
-          "@id": "kernels-labeled/kernel_family",
-          "name": "kernel_family",
-          "dataType": "sc:Text",
-          "source": {
-            "fileObject": {
-              "@id": "kernels-labeled-parquet"
-            },
-            "extract": {
-              "column": "kernel_family"
-            }
-          }
-        },
-        {
-          "@type": "cr:Field",
-          "@id": "kernels-labeled/kernel_name",
-          "name": "kernel_name",
-          "dataType": "sc:Text",
-          "source": {
-            "fileObject": {
-              "@id": "kernels-labeled-parquet"
-            },
-            "extract": {
-              "column": "kernel_name"
-            }
-          }
-        },
-        {
-          "@type": "cr:Field",
-          "@id": "kernels-labeled/gpu_time_duration_ms",
-          "name": "gpu_time_duration_ms",
-          "dataType": "sc:Float",
-          "source": {
-            "fileObject": {
-              "@id": "kernels-labeled-parquet"
-            },
-            "extract": {
-              "column": "gpu_time_duration_ms"
-            }
-          }
-        },
-        {
-          "@type": "cr:Field",
-          "@id": "kernels-labeled/dram_bytes_sum",
-          "name": "dram_bytes_sum",
-          "dataType": "sc:Float",
-          "source": {
-            "fileObject": {
-              "@id": "kernels-labeled-parquet"
-            },
-            "extract": {
-              "column": "dram_bytes_sum"
-            }
-          }
-        }
-      ]
-    }
-  ],
-  "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"
-    },
-    {
-      "@id": "https://github.com/TerminalBench/TerminalBench",
-      "name": "TerminalBench"
-    },
-    {
-      "@id": "https://github.com/xlang-ai/OSWorld",
-      "name": "OSWorld"
-    },
-    {
-      "@id": "https://huggingface.co/datasets/Aeala/ShareGPT_Vicuna_unfiltered",
-      "name": "ShareGPT"
-    }
-  ],
-  "prov:wasGeneratedBy": {
-    "@type": "prov:Activity",
-    "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."
-  },
-  "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. 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."
-}
+{
+  "@context": {
+    "@language": "en",
+    "@vocab": "https://schema.org/",
+    "citeAs": "cr:citeAs",
+    "column": "cr:column",
+    "conformsTo": "dct:conformsTo",
+    "cr": "http://mlcommons.org/croissant/",
+    "data": {
+      "@id": "cr:data",
+      "@type": "@json"
+    },
+    "dataType": {
+      "@id": "cr:dataType",
+      "@type": "@vocab"
+    },
+    "dct": "http://purl.org/dc/terms/",
+    "equivalentProperty": "cr:equivalentProperty",
+    "examples": {
+      "@id": "cr:examples",
+      "@type": "@json"
+    },
+    "extract": "cr:extract",
+    "field": "cr:field",
+    "fileObject": "cr:fileObject",
+    "fileProperty": "cr:fileProperty",
+    "fileSet": "cr:fileSet",
+    "format": "cr:format",
+    "includes": "cr:includes",
+    "isLiveDataset": "cr:isLiveDataset",
+    "jsonPath": "cr:jsonPath",
+    "key": "cr:key",
+    "md5": "cr:md5",
+    "parentField": "cr:parentField",
+    "path": "cr:path",
+    "rai": "http://mlcommons.org/croissant/RAI/",
+    "recordSet": "cr:recordSet",
+    "references": "cr:references",
+    "regex": "cr:regex",
+    "repeated": "cr:repeated",
+    "replace": "cr:replace",
+    "samplingRate": "cr:samplingRate",
+    "prov": "http://www.w3.org/ns/prov#",
+    "sc": "https://schema.org/",
+    "separator": "cr:separator",
+    "source": "cr:source",
+    "subField": "cr:subField",
+    "transform": "cr:transform"
+  },
+  "@type": "sc:Dataset",
+  "name": "AgentPerfBench",
+  "description": "LLM inference benchmark dataset measuring serving performance (TTFT, TPOT, throughput) across 9 models, 4 GPU platforms, 2 serving engines under agentic and chat workloads. Three configurations: trace_replay, distributional, and kernels_labeled.",
+  "url": "https://huggingface.co/datasets/agent-perf-bench/AgentPerfBench",
+  "license": "https://spdx.org/licenses/Apache-2.0.html",
+  "conformsTo": "http://mlcommons.org/croissant/1.1",
+  "datePublished": "2026-05-04",
+  "version": "1.0",
+  "citeAs": "@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}}",
+  "creator": {
+    "@type": "sc:Organization",
+    "name": "Anonymous (under review)"
+  },
+  "distribution": [
+    {
+      "@type": "cr:FileObject",
+      "@id": "trace-replay-summary-parquet",
+      "name": "trace_replay/summary.parquet",
+      "contentUrl": "https://huggingface.co/datasets/agent-perf-bench/AgentPerfBench/resolve/main/trace_replay/summary.parquet",
+      "encodingFormat": "application/x-parquet",
+      "sha256": "19dfc3ed8e2f09c52449045cded6bce29a00c0b9c4e563a407ed8e9960333da3"
+    },
+    {
+      "@type": "cr:FileObject",
+      "@id": "distributional-summary-parquet",
+      "name": "distributional/summary.parquet",
+      "contentUrl": "https://huggingface.co/datasets/agent-perf-bench/AgentPerfBench/resolve/main/distributional/summary.parquet",
+      "encodingFormat": "application/x-parquet",
+      "sha256": "67d63072831143e929e0b6d6464744756f783b53ac30fed0232bc01c601676ec"
+    },
+    {
+      "@type": "cr:FileObject",
+      "@id": "kernels-labeled-parquet",
+      "name": "kernel_profiles/kernels_labeled.parquet",
+      "contentUrl": "https://huggingface.co/datasets/agent-perf-bench/AgentPerfBench/resolve/main/kernel_profiles/kernels_labeled.parquet",
+      "encodingFormat": "application/x-parquet",
+      "sha256": "05571ee61fe97beac7f3d0b0e8be07a0feeca00cee9b308cd469a57cd4c9b671"
+    }
+  ],
+  "recordSet": [
+    {
+      "@type": "cr:RecordSet",
+      "@id": "trace-replay-summary",
+      "name": "Trace Replay Summary",
+      "description": "One row per benchmark configuration from trace replay runs (empirical agent session replays).",
+      "field": [
+        {
+          "@type": "cr:Field",
+          "@id": "trace-replay-summary/run_id",
+          "name": "run_id",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "trace-replay-summary-parquet"
+            },
+            "extract": {
+              "column": "run_id"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "trace-replay-summary/model",
+          "name": "model",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "trace-replay-summary-parquet"
+            },
+            "extract": {
+              "column": "model"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "trace-replay-summary/hardware",
+          "name": "hardware",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "trace-replay-summary-parquet"
+            },
+            "extract": {
+              "column": "hardware"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "trace-replay-summary/engine",
+          "name": "engine",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "trace-replay-summary-parquet"
+            },
+            "extract": {
+              "column": "engine"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "trace-replay-summary/profile",
+          "name": "profile",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "trace-replay-summary-parquet"
+            },
+            "extract": {
+              "column": "profile"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "trace-replay-summary/concurrency",
+          "name": "concurrency",
+          "dataType": "sc:Integer",
+          "source": {
+            "fileObject": {
+              "@id": "trace-replay-summary-parquet"
+            },
+            "extract": {
+              "column": "concurrency"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "trace-replay-summary/request_throughput",
+          "name": "request_throughput",
+          "dataType": "sc:Float",
+          "source": {
+            "fileObject": {
+              "@id": "trace-replay-summary-parquet"
+            },
+            "extract": {
+              "column": "request_throughput"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "trace-replay-summary/median_ttft_ms",
+          "name": "median_ttft_ms",
+          "dataType": "sc:Float",
+          "source": {
+            "fileObject": {
+              "@id": "trace-replay-summary-parquet"
+            },
+            "extract": {
+              "column": "median_ttft_ms"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "trace-replay-summary/median_tpot_ms",
+          "name": "median_tpot_ms",
+          "dataType": "sc:Float",
+          "source": {
+            "fileObject": {
+              "@id": "trace-replay-summary-parquet"
+            },
+            "extract": {
+              "column": "median_tpot_ms"
+            }
+          }
+        }
+      ]
+    },
+    {
+      "@type": "cr:RecordSet",
+      "@id": "distributional-summary",
+      "name": "Distributional Summary",
+      "description": "One row per benchmark configuration from distributional runs (statistical sampling from lognormal fits to recorded workload statistics).",
+      "field": [
+        {
+          "@type": "cr:Field",
+          "@id": "distributional-summary/run_id",
+          "name": "run_id",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "distributional-summary-parquet"
+            },
+            "extract": {
+              "column": "run_id"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "distributional-summary/model",
+          "name": "model",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "distributional-summary-parquet"
+            },
+            "extract": {
+              "column": "model"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "distributional-summary/hardware",
+          "name": "hardware",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "distributional-summary-parquet"
+            },
+            "extract": {
+              "column": "hardware"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "distributional-summary/engine",
+          "name": "engine",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "distributional-summary-parquet"
+            },
+            "extract": {
+              "column": "engine"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "distributional-summary/profile",
+          "name": "profile",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "distributional-summary-parquet"
+            },
+            "extract": {
+              "column": "profile"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "distributional-summary/concurrency",
+          "name": "concurrency",
+          "dataType": "sc:Integer",
+          "source": {
+            "fileObject": {
+              "@id": "distributional-summary-parquet"
+            },
+            "extract": {
+              "column": "concurrency"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "distributional-summary/request_throughput",
+          "name": "request_throughput",
+          "dataType": "sc:Float",
+          "source": {
+            "fileObject": {
+              "@id": "distributional-summary-parquet"
+            },
+            "extract": {
+              "column": "request_throughput"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "distributional-summary/median_ttft_ms",
+          "name": "median_ttft_ms",
+          "dataType": "sc:Float",
+          "source": {
+            "fileObject": {
+              "@id": "distributional-summary-parquet"
+            },
+            "extract": {
+              "column": "median_ttft_ms"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "distributional-summary/median_tpot_ms",
+          "name": "median_tpot_ms",
+          "dataType": "sc:Float",
+          "source": {
+            "fileObject": {
+              "@id": "distributional-summary-parquet"
+            },
+            "extract": {
+              "column": "median_tpot_ms"
+            }
+          }
+        }
+      ]
+    },
+    {
+      "@type": "cr:RecordSet",
+      "@id": "kernels-labeled",
+      "name": "Kernels Labeled",
+      "description": "Per-kernel Nsight Compute (ncu) hardware counter profiles. Individual kernel invocations across 4 GPUs and 13 model/sweep sources.",
+      "field": [
+        {
+          "@type": "cr:Field",
+          "@id": "kernels-labeled/source",
+          "name": "source",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "kernels-labeled-parquet"
+            },
+            "extract": {
+              "column": "source"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "kernels-labeled/gpu",
+          "name": "gpu",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "kernels-labeled-parquet"
+            },
+            "extract": {
+              "column": "gpu"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "kernels-labeled/model",
+          "name": "model",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "kernels-labeled-parquet"
+            },
+            "extract": {
+              "column": "model"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "kernels-labeled/kernel_family",
+          "name": "kernel_family",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "kernels-labeled-parquet"
+            },
+            "extract": {
+              "column": "kernel_family"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "kernels-labeled/kernel_name",
+          "name": "kernel_name",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "kernels-labeled-parquet"
+            },
+            "extract": {
+              "column": "kernel_name"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "kernels-labeled/gpu_time_duration_ms",
+          "name": "gpu_time_duration_ms",
+          "dataType": "sc:Float",
+          "source": {
+            "fileObject": {
+              "@id": "kernels-labeled-parquet"
+            },
+            "extract": {
+              "column": "gpu_time_duration_ms"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "kernels-labeled/dram_bytes_sum",
+          "name": "dram_bytes_sum",
+          "dataType": "sc:Float",
+          "source": {
+            "fileObject": {
+              "@id": "kernels-labeled-parquet"
+            },
+            "extract": {
+              "column": "dram_bytes_sum"
+            }
+          }
+        }
+      ]
+    }
+  ],
+  "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"
+    },
+    {
+      "@id": "https://github.com/TerminalBench/TerminalBench",
+      "name": "TerminalBench"
+    },
+    {
+      "@id": "https://github.com/xlang-ai/OSWorld",
+      "name": "OSWorld"
+    },
+    {
+      "@id": "https://huggingface.co/datasets/Aeala/ShareGPT_Vicuna_unfiltered",
+      "name": "ShareGPT"
+    }
+  ],
+  "prov:wasGeneratedBy": {
+    "@type": "prov:Activity",
+    "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."
+  },
+  "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."
+}

distributional/summary.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:bb9a48351e0b612b3125c5d0a8e200638d790269f821e45aae342a4042bfd951
-size 70836
+oid sha256:67d63072831143e929e0b6d6464744756f783b53ac30fed0232bc01c601676ec
+size 68747

trace_replay/summary.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a9cd2565a9292e75f54791e8108f2491b7bb371fdfeeefb81cdb00833860dd23
-size 694254
+oid sha256:19dfc3ed8e2f09c52449045cded6bce29a00c0b9c4e563a407ed8e9960333da3
+size 633996