deepseek-ai/DeepGEMM-Ascend
DeepGEMM-Ascend: clean and efficient matrix multiplication kernel library for Huawei Ascend NPUs
From the publisher
README & documentation
Source previewRead the project’s overview, installation instructions and usage examples. The original README is the source of truth.
DeepGEMM Ascend is a port of DeepGEMM to the HUAWEI Ascend platform. It is fully API-compatible with DeepGEMM and supports BF16, FP8, FP4 GEMM, MQA logits, and MegaMoE. On Ascend platforms, users can simply install the package and use the same APIs and development workflow as DeepGEMM on other supported platforms. DeepGEMM Ascend provides a lightweight abstraction over the Ascend MAD (matrix multiply-add) primitives, hiding much of the complexity associated with fractal layouts, alignment constraints, address calculations, and verbose low-level parameters. This enables GEMM kernels to remain both concise and efficient. DeepGEMM Ascend makes extensive use of Ascend-specific optimization techniques, such as sparse data loading and coroutine-based pipelining, to approach the performance limits of the Ascend hardware. These implementations can also serve as references for extreme performance optimization on the Ascend platform. Despite its lightweight codebase, DeepGEMM Ascend can achieve peak hardware performance across a wide range of matrix shapes.…
Read the full README ↗ · Preview checked 2026-09-30T12:11:15.286Z
Inside the original README — Document outline
- DeepGEMM Ascend
- News
- Quick Start
- Requirements
- Development
- Installation
- Interfaces
- Kernel Interface
- Utilities
- Environment Variables
- Performance
- Dense GEMM
Headings are captured from the source. Links open the publisher’s document, not a locally hosted copy.
Links from the README
References supplied by the publisher, not independently verified endorsements. Check the destination before downloading files or entering credentials.
Documentation belongs to its respective authors. Reported project/model license: MIT. A listing is not a grant of reuse or training rights. Confirm the document’s own terms at the source.
What this repository does
DeepGEMM-Ascend: clean and efficient matrix multiplication kernel library for Huawei Ascend NPUs
Repository facts
- Owner
- deepseek-ai
- Primary language
- C++
- Stars
- 295
- Forks
- 12
- Open issues + pull requests
- 2
- License
- MIT
- Archived
- No
- Default branch
- main
- Created
- 2026-09-29T15:49:55.000Z
- Last push
- 2026-09-30T01:11:39.000Z
Topics and intended use
No topics were included in the latest source metadata.
Review the README for scope, installation, examples and limitations. We do not run repository code or certify it.
Evaluate before installing
Review licensing and dependencies, inspect recent commits and unresolved issues, and test in an isolated environment before production use. Stars and forks alone cannot answer these questions.
Source and freshness
Source: GitHub. Metadata observed 2026-09-30T12:05:16.119Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.