DeepGEMM
DeepSeekDeepGEMM is a tensor-core kernel library gathering the computation primitives modern large language models depend on - FP8, FP4 and BF16 GEMMs, fused mixture-of-experts with overlapped communication, and MQA scoring - into one CUDA codebase. Kernels compile at runtime through a lightweight JIT module, so installation needs no CUDA toolchain. It borrows concepts from CUTLASS and CuTe without depending on their templates.
Verified 2026-08-18 via the GitHub API and the LICENSE body.
Openness
5 high confidence- license
- MIT(OSI)
- source
- public
- core-gated
- ungated
MIT license body confirmed. The repository is public and unarchived and builds the whole product, and the README describes no paid tier, enterprise edition or license-gated build beside it, so source is public and the core ungated.
- https://github.com/deepseek-ai/DeepGEMM/blob/main/LICENSE recorded 2026-08-18
LICENSE file is the verbatim MIT text
- https://api.github.com/repos/deepseek-ai/DeepGEMM recorded 2026-08-18
Repo metadata - license spdx_id MIT, private false, archived false, default branch main - for deepseek-ai/DeepGEMM.
- https://github.com/deepseek-ai/DeepGEMM/blob/main/README.md recorded 2026-08-18
README describes a unified tensor-core kernel library covering FP8, FP4 and BF16 GEMMs, fused MoE with overlapped communication and MQA scoring, all compiled at runtime through a lightweight JIT module, with no paid tier, enterprise edition or license-key-gated build beside it.
Adoption
2 low confidence7,700 GitHub stars, which lands in the 1K-10K stars band of the stars scale, level 2. Built from source rather than published to a package registry, so no download figure exists and the stars scale applies with its cap of 3.
- https://api.github.com/repos/deepseek-ai/DeepGEMM recorded 2026-08-18
Repo metadata - stargazers_count = 7,700 - for deepseek-ai/DeepGEMM.
Capability
3 medium confidenceBanded on the category feature matrix as performance-critical kernel and operator library. Placed two bands below the apache-tvm anchor, on a matrix that bands on how much of the model-to-hardware transformation pipeline a product performs, over how many inputs and targets.
- https://github.com/deepseek-ai/DeepGEMM/blob/main/README.md recorded 2026-08-18
README still documents a unified tensor-core kernel library covering FP8, FP4 and BF16 GEMMs, fused MoE with overlapped communication and MQA scoring, all compiled at runtime through a lightweight JIT module.
Verified 2026-08-18