metadata
dataset_info:
splits:
- name: train
num_examples: 594
configs:
- config_name: default
data_files:
- split: train
path: data/train-00000-of-00001.parquet
ISO-Bench Pass@k GPU Benchmark Results
Agent-generated optimization patches benchmarked on real GPU hardware (NVIDIA H100 80GB).
Scope: Lossfunk/ISO-Bench — 54 tasks (39 vLLM + 15 SGLang) Patches from: Inferencebench/pass-at-k-samples
Summary
| vLLM | SGLang | Total | |
|---|---|---|---|
| Tasks benchmarked | 29/39 | 10/15 | 39/54 |
| Successful benchmarks | 444 | 150 | 594 |
| Agent | vLLM | SGLang | Total |
|---|---|---|---|
| Claude Code (Sonnet 4.5) | 230 | 76 | 306 |
| Codex CLI (GPT-5) | 214 | 74 | 288 |
Schema
| Column | Type | Description |
|---|---|---|
item_id |
str | ISO-Bench task ID (e.g., vllm_core-0000) |
sample_index |
int | Pass@k sample index (0-7) |
repo |
str | vllm or sglang |
agent_name |
str | claude_code or codex_cli |
agent_model |
str | sonnet-4.5 or gpt-5 |
human_commit |
str | Human optimization commit hash |
parent_commit |
str | Baseline (pre-optimization) commit hash |
benchmark_mode |
str | serving, standalone, prefix_caching, offline |
llm_model |
str | LLM model benchmarked (e.g., meta-llama/Meta-Llama-3-8B-Instruct) |
duration_s |
float | Total benchmark duration (seconds) |
Serving Metrics
ttft_mean_ms, ttft_median_ms, ttft_p99_ms — Time To First Token
tpot_mean_ms, tpot_median_ms, tpot_p99_ms — Time Per Output Token
itl_mean_ms, itl_median_ms, itl_p99_ms — Inter-Token Latency
request_throughput_req_s — Requests/sec
output_token_throughput_tok_s — Output tokens/sec
total_token_throughput_tok_s — Total tokens/sec
Latency Metrics
latency_avg_ms, latency_p50_ms, latency_p99_ms
Throughput Metrics
throughput_tok_s, elapsed_time_s, input_throughput_tok_s
Usage
from datasets import load_dataset
ds = load_dataset("Inferencebench/pass-at-k-benchmark-results", split="train")
# vLLM Claude Code results
vllm_cc = ds.filter(lambda x: x["repo"] == "vllm" and x["agent_name"] == "claude_code")
# SGLang Codex results
sg_cx = ds.filter(lambda x: x["repo"] == "sglang" and x["agent_name"] == "codex_cli")
# Compute pass@k for a specific task
task = ds.filter(lambda x: x["item_id"] == "vllm_core-0005")
Hardware & Method
- GPU: NVIDIA H100 80GB PCIe
- vLLM: Docker containers from
shikhar481/vllm_fixed_human_images(baseline images) - SGLang: Docker containers from
ayushnangia16/nvidia-sglang-docker - Execution: Persistent container per task, sequential samples, setup shared across agents
- Date: 2026-03-29 to 2026-03-31