ScalingIntelligence/KernelBench resource

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1214 stars · 192 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

KernelBench is a benchmark and toolkit from Stanford's Scaling Intelligence Lab that evaluates whether LLMs can generate correct and efficient GPU kernels from PyTorch programs. It provides 250+ tasks across four difficulty levels, evaluation scripts for correctness and speedup checks, and a HuggingFace dataset.

Use cases

• benchmark llm code generation for cuda kernels
• evaluate if a model can write efficient gpu kernels
• measure speedup of llm-generated kernels against pytorch baselines
• use kernel optimization tasks as an rl environment for training models
• compare codegen models on gpu kernel transpilation
• optimize pytorch operators with custom cuda kernels

When to choose

• you need a standardized benchmark for llm gpu kernel generation
• you want to evaluate or train models on cuda code generation tasks
• you are researching agentic kernel optimization with profiling feedback

When to avoid

• you need a production tool to auto-generate kernels for your app
• you have no gpu hardware available
• you want a turnkey agentic scaffold rather than a benchmark toolkit

Facets

dataset · maturity active

benchmarking llm-training machine-learning gpu-computing large-language-models machine-learning gpu-computing developer-tools artificial-intelligence python cli llm-benchmark cuda-codegen kernel-generation code-evaluation rl-environment pytorch evaluation gpu linux

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