Skip to content

Update instructions for SGLang deepgemm fork development #40

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
Fridge003 merged 1 commit into from
Jun 3, 2026
Merged

Update instructions for SGLang deepgemm fork development #40

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
8 changes: 8 additions & 0 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,5 +1,13 @@
# DeepGEMM

## Notice for SGLang DeepGemm Fork Developers

- `main` tracks upstream updates and should stay clean.
- `dev` is the active SGL integration branch and can be force-updated during rebases. Please open pull requests to this branch
- `release/*` branches are archives for past wheel releases and shouldn't be modified.

For more instructions, please check this [document](https://github.com/sgl-project/DeepGEMM/blob/dev/sgl_deep_gemm/README.md).

DeepGEMM is a unified, high-performance tensor core kernel library that brings together the key computation primitives of modern large language models — GEMMs (FP8, FP4, BF16), fused MoE with overlapped communication (Mega MoE), MQA scoring for the lightning indexer, HyperConnection (HC), and more — into a single, cohesive CUDA codebase. All kernels are compiled at runtime via a lightweight Just-In-Time (JIT) module, requiring no CUDA compilation during installation.

DeepGEMM leverages some concepts from [CUTLASS](https://github.com/nvidia/cutlass) and [CuTe](https://github.com/NVIDIA/cutlass/tree/main/include/cute), but avoids heavy reliance on their templates or algebras. The library is designed for simplicity, with only a limited number of core kernel functions, making it a clean and accessible resource for learning NVIDIA GPU kernel optimization techniques.
Expand Down
Loading