license: apache-2.0
KBench — GPU kernel-generation benchmark
400 tasks. Each hands the agent a correct-but-slow reference implementation and an empty stub; the agent writes a fast GPU kernel.
reward = 0 if the submission is incorrect
reward = achieved TFLOP/s or GB/s otherwise, UNCAPPED
Correctness is the gate; speed is the reward. There is no oracle and no gold solution — the score is an absolute hardware metric, so it is hardware-portable by construction and nothing has to be re-benchmarked when the hardware changes.
Layout
tasks/<family>/<task>/
instruction.md what the agent reads
task.toml metadata (timeouts, GPUs, keywords)
environment/ Dockerfile, reference.py, the stub the agent edits, measure.py
tests/ the grader (verify_env.py) + test.sh
tools/ the generators that produce these tasks
agent/ a kernel-optimization skill + subagents for solving these tasks
Running one task
cd tasks/<family>/<task>
docker build -t mytask environment
docker run --rm --gpus device=0 -v $PWD/tests:/tests:ro mytask bash /tests/test.sh
The grader writes reward.json / details.json to /logs/verifier/. Running it as above with the untouched stub scores 0; that is the intended starting point.
Families
| family | tasks | what it covers |
|---|---|---|
megakernel |
26 | Whole-model decode fused into one persistent kernel, plus the primitives that make it possible. |
image-flux-sd3-mmdit |
16 | FLUX. |
video-cogvideox-mochi-ltx |
14 | Video DiTs with structurally different designs from Wan and HunyuanVideo: CogVideoX's separate LayerNorm experts for text vs video, Mochi's asymmetric joint attention over unequal per-stream head dims, and LTX's very high compression VAE. |
video-wan-dit |
15 | Wan 2. |
video-hunyuanvideo-mmdit |
15 | HunyuanVideo's MMDiT: dual-stream blocks (separate QKV/norm/FFN for text and video, joint attention) then single-stream. |
video-3d-causal-vae |
22 | The 3D causal VAE / tokenizer both Wan and HunyuanVideo use — 4x8x8 compression, one-sided temporal padding, tiled decode. |
video-sparse-attention |
26 | Published sparse-attention schemes for video diffusion: Sliding Tile Attention, Sparse VideoGen head routing, Radial Attention, and block-sparse / caching variants. |
diffusion-sampling |
14 | Flow-matching and DPM-Solver++ steps, TeaCache-style skip decisions, latent blending. |
multimodal-audio |
16 | Vision encoders, multimodal RoPE, and audio/speech synthesis kernels. |
linear-attention-ssm |
48 | Linear-attention and state-space architectures: Gated DeltaNet, Mamba-2, RWKV-7, GLA, GSA, Comba, MesaNet, TTT, Titans and relatives. |
attention-text-llm |
28 | Attention variants from text LLMs: MLA, NSA, MoBA, DSA, cascade, chunked prefill. |
quantization-gemm |
44 | FP8, MXFP4, NVFP4, AWQ, GPTQ, INT8 and INT4 GEMM and quantisation kernels. |
moe |
20 | Mixture-of-experts routing, permutation, grouped GEMM and combine. |
kv-cache-paging |
25 | Paged KV allocation, append, compaction, eviction and quantisation. |
training-optimizer-rl |
34 | Backward passes, fused optimisers, and RLHF / distillation losses. |
sampling-speculative-decoding |
10 | Top-k/top-p/min-p filtering, beam search, and speculative decoding. |
norm-rope-fusion |
7 | RMSNorm / LayerNorm and RoPE fusions. |
distributed-multi-gpu |
8 | Two-GPU tasks. |
raw-cuda |
12 | Raw-CUDA tasks. |
CATALOG.json has the same information machine-readably: family, metric, roofline, GPU count and keywords for every task.
How tasks are graded
Correctness is checked against a private copy of the reference embedded in the grader, so editing environment/reference.py cannot affect the score. Every timed repetition runs on freshly generated inputs and the last one is re-validated, so memoising a result and replaying it fails the gate instead of posting an inflated number.
Work is attributed by a formula that depends only on the shape, never on the implementation, so every submission is credited identically and the ranking is a pure speed ranking.
Tolerances are measured, not guessed. Each one is set from the relative error between the reference and an independent correct implementation (the numerical floor), and checked against a variant that ignores the task's distinguishing feature. The measured numbers are written into each task's precision section.
Sizing. Every task's roofline time at its largest graded shape is above 250 us, so the kernel dominates rather than launch overhead.
Toolchain
Tasks are offline: no internet at run time, and no flashinfer, vLLM, flash-attn or TensorRT-LLM is installed. Agents are expected to write CUDA C++ (nvcc and CUTLASS are present) or Triton. torch is available for setup and where there is no efficient direct alternative. Nothing scans submitted source — restrictions are enforced by what is installed and, for the megakernel family, by measuring how the submission actually executes.