kernel
Datasets
All datasets matching “kernel”kernelbench-samples
KernelBench Samples
Samples from experiments for KernelBench, described in our arxiv
Learn more about KernelBench from our
Paper
Github Repo
The samples are organized as such
baseline_eval (Section 4 Baseline)
repeated_sampling (Section 5.1.1 Repeated Sampling)
iterative_refinement (Section 5.1.2 Iterative Refinement of Generations)
Within each folder, we organize the results by /level/model/problem_{id}/sample_{id}.
The inner most .json file contains the generated kernel and… See the full description on the dataset page: https://huggingface.co/datasets/ScalingIntelligence/kernelbench-samples.kernelbench-mega-traces
KernelBench-Mega agent traces
Coding agents writing full GPU megakernels across Blackwell / H100 / B200, scored as speedup over reference; contamination-audited (23 verified cells).
Each .jsonl file is one agent run in Claude-Code session format, viewable with the agent trace viewer. Filename = run id; manifest.csv maps each run to model / harness / problem / GPU / score.
23 agent traces · live leaderboard: https://kernelbench.com/mega
Secrets redacted. Full reasoning for… See the full description on the dataset page: https://huggingface.co/datasets/Infatoshi/kernelbench-mega-traces.open-license-corpus
PubText
Welcome to the Open License Corpus (OLC), a 228B token corpus for training permissively-licensed language models.
Disclaimer: OLC should not be considered a universally safe-to-use dataset. We encourage users of OLC to consult a legal professional on the suitability of each data source for their application.
Dataset Summary
Domain
Sources
Specific License
# BPE Tokens (in billions; GPT-NeoX tokenizer)
Legal
Case Law, Pile of Law (PD subset)
Public… See the full description on the dataset page: https://huggingface.co/datasets/kernelmachine/open-license-corpus.kernelbench-hard-traces
KernelBench-Hard agent traces
Frontier coding agents writing optimized CUDA/Triton kernels (FP8 GEMM, paged
attention, MoE, W4A16, KDA, Top-k) on RTX PRO 6000 Blackwell, H100 PCIe, and
B200; roofline-graded.
Each .jsonl file is one agent run in Claude-Code session format, viewable with
the Hugging Face Agent Trace viewer (Data Studio → open a row). Filename =
run id.
Live leaderboard: https://kernelbench.com/hard
Secrets redacted. Full reasoning for open-provider routes… See the full description on the dataset page: https://huggingface.co/datasets/Infatoshi/kernelbench-hard-traces.kernelbench-v3-runs
KernelBench-v3 — Agent Runs
2071 agent evaluations from the v3 sweep (2026-02): 10 frontier models × {RTX 3090, H100, B200} × 43–58 problems per GPU. Each row is one (model, gpu, problem) triple with correctness, speedup, baseline timing, token usage, cost, and a pointer to the agent's winning solution.py.
Companion datasets:
Infatoshi/kernelbench-v3-problems — 60 problem definitions
Infatoshi/kernelbench-hard-runs — newer KernelBench-Hard sweep (12 models × 7 problems on Blackwell… See the full description on the dataset page: https://huggingface.co/datasets/Infatoshi/kernelbench-v3-runs.KernelBench
KernelBench
A benchmark designed to evaluate the ability of LLMs to generate efficient GPU kernels for optimizing neural network performance
Version
[07-21-2025] This HF dataset version has been updated to v0.1
Citation
@misc{ouyang2024kernelbench,
title={KernelBench: Can LLMs Write GPU Kernels?},
author={Anne Ouyang and Simon Guo and Azalia Mirhoseini},
year={2024},
url={https://scalingintelligence.stanford.edu/blogs/kernelbench/},
}