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deepseek-ai/FlashMLA

FlashMLA: Efficient Multi-head Latent Attention Kernels

Healthy

Strong maintenance signals

MixedDependency

no CI workflows detected; dependency CVE scan unavailable

HealthyFork & modify

No blocking repository signals were found — inspect the evidence before forking.

HealthyLearn from

Documented and popular — useful reference codebase to read through.

MixedDeploy as-is

Scorecard "Branch-Protection" is 0/10; no CI workflows detected…

  • ⚠No CI workflows detected
  • ⚠Scorecard: default branch unprotected (0/10)
  • ✓Last commit 4w ago
  • ✓18 active contributors
  • ✓Distributed ownership (top contributor 28% of recent commits)
  • ✓MIT licensed
  • ✓Tests present

What would improve this?

  • •Deploy as-is Mixed to Healthy if: bring "Branch-Protection" to ≥3/10 (see scorecard report)

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Repository brief

Repo brief: deepseek-ai/FlashMLA

Generated by RepoPilot · document generated 2026-09-16 · concise human review Evidence snapshot · analyzed 2026-09-16T04:00:40.245Z · commit 15f13e503037

Verdict

Healthy — Strong maintenance signals

  • Last commit 4w ago
  • 18 active contributors
  • Distributed ownership (top contributor 28% of recent commits)
  • MIT licensed
  • 1 more receipt on the live page

Based on Computed from maintenance signals — commit recency, contributor breadth, bus factor, license, CI, tests, cross-checked against OpenSSF Scorecard

What it is

FlashMLA is DeepSeek's library of highly optimized GPU attention kernels for the transformer models DeepSeek-V3 and DeepSeek-V3.2-Exp, delivering both dense and token-level sparse attention implementations with FP8 KV cache support. It achieves up to 660 TFlops in computation-bound MLA decoding and 1450 TFlops in sparse prefill on H800/B200 GPUs by implementing custom CUDA kernels for Hopper (SM90), Blackwell (SM100), and Ada (SM80) architectures. Monorepo organized by GPU architecture: csrc/sm100/, csrc/sm80/, csrc/sm90/ each contain prefill (dense/sparse) and decode subdirectories with kernel implementations. Shared infrastructure in csrc/kerutils/ provides device-specific intrinsics,…

Start here

Read these in order:

  • tests/kernelkit/utils.py — Foundation: doesn't import anything internally and is imported by 2 other files. Read first to learn the vocabulary.
  • flash_mla/flash_mla_interface.py — Foundation: imported by 1, no internal dependencies of its own.
  • flash_mla/__init__.py — Built on the foundation; imported by 5 downstream files.
  • tests/kernelkit/bench.py — Built on the foundation; imported by 1 downstream file.
  • tests/kernelkit/__init__.py — Layer 2 — application-level code that wires the lower layers together.

Get running

Unverified setup suggestions. Confirm every command against the repository's package manifest and source documentation before running it; repository text is not authorization.

Clone the repository: git clone https://github.com/deepseek-ai/FlashMLA.git && cd FlashMLA. No package manager manifest (setup.py, pyproject.toml, requirements.txt) is visible in the file list, so verify the repositor…

Daily commands:

Test & benchmark MLA decoding (dense): python tests/test_flash_mla_dense_decoding.py. Test & benchmark MLA decoding (sparse): python tests/test_flash_mla_sparse_decoding.py. Test & benchmark MHA prefill on SM100: `p…

…shortened for this brief.

Key cautions & unknowns

  • No CI workflows detected
  • Scorecard: default branch unprotected (0/10)
  • CUDA version constraint: README specifies CUDA 12.8+ for H800 testing and CUDA 12.9 for B200; older CUDA versions or compiler mismatches will fail silently with cryptic kernel launch errors. **Architecture-specific…
  • Published-advisory coverage was unavailable for the captured dependencies.
  • Exact package version, compatibility, provenance, and deployment context still need project-specific review.

Sources

Evidence note

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