FlashMLA is DeepSeek's MIT-licensed CUDA kernel library for optimized attention in DeepSeek-V3 and DeepSeek-V3.2-Exp style inference. It includes dense MLA decoding plus sparse attention kernels for DeepSeek Sparse Attention, with README-reported H800/CUDA metrics up to 3000 GB/s, 660 TFLOPS, and sparse 640/410 TFlops paths. It has 12.7K+ GitHub stars.
Alternatives to DeepGEMM
2 editor-selected alternatives · DeepGEMM overview →
source: tools.alternatives · stored order · active records only; review scores are annotations and never change membership or order
A directional evidence panel appears only when the substitute rationale, trade-offs, sources, and verification date have been recorded. Older selections without that panel remain visible but are unclassified under the new evidence contract.
DeepEP is DeepSeek's open-source communication library optimized for expert-parallel training of Mixture-of-Experts models. It provides efficient GPU-to-GPU data routing for distributing tokens to expert networks across multiple devices during MoE model training and inference. Enables the distributed expert parallelism that powers DeepSeek's competitive model efficiency. Over 9,100 GitHub stars.
Open-source DeepGEMM alternatives
FlashMLA, DeepEP — see all open-source developer tools.
More DevOps & Deployment tools
same category, not editor-selected alternatives — see how DeepGEMM compares →
FAQ
Which DeepGEMM alternative is listed first?
FlashMLA is first in the editor-selected list of 2 DeepGEMM alternatives and carries an editorial review score of 80/100. The stored order is editorial; review scores do not determine membership or position.
Are there open-source DeepGEMM alternatives?
Yes — FlashMLA, DeepEP are open source.