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 DeepEP
2 editor-selected alternatives · DeepEP 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.
DeepGEMM is DeepSeek's open-source library of FP8 matrix multiplication CUDA kernels optimized for LLM inference and training on modern NVIDIA GPUs. It provides efficient GEMM operations using 8-bit floating point precision that reduce memory bandwidth requirements while maintaining model accuracy. Designed for integration into inference engines and training frameworks. Over 6,300 GitHub stars.
Open-source DeepEP alternatives
FlashMLA, DeepGEMM — see all open-source developer tools.
More DevOps & Deployment tools
same category, not editor-selected alternatives — see how DeepEP compares →
FAQ
Which DeepEP alternative is listed first?
FlashMLA is first in the editor-selected list of 2 DeepEP 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 DeepEP alternatives?
Yes — FlashMLA, DeepGEMM are open source.