FlashMLA

FlashMLA is an efficient MLA decoding kernel for Hopper GPUs, optimized for variable-length sequences serving.

Currently released:

• BF16, FP16
• Paged kvcache with block size of 64

Quick start

Install

python setup.py install

Benchmark

python tests/test_flash_mla.py

Achieving up to 3000 GB/s in memory-bound configuration and 580 TFLOPS in computation-bound configuration on H800 SXM5, using CUDA 12.8.

Usage

from flash_mla import get_mla_metadata, flash_mla_with_kvcache

tile_scheduler_metadata, num_splits = get_mla_metadata(cache_seqlens, s_q * h_q // h_kv, h_kv)

for i in range(num_layers):
    ...
    o_i, lse_i = flash_mla_with_kvcache(
        q_i, kvcache_i, block_table, cache_seqlens, dv,
        tile_scheduler_metadata, num_splits, causal=True,
    )
    ...

Requirements

• Hopper GPUs
• CUDA 12.3 and above
  • But we highly recommend 12.8 or above for the best performance
• PyTorch 2.0 and above

Acknowledgement

FlashMLA is inspired by FlashAttention 2&3 and cutlass projects.

Community Support

MetaX

For the MetaX GPU【https://www.metax-tech.com】, the corresponding FlashMLA version link is as follows: GitHub - MetaX-MACA/FlashMLA

Moore Threads (WIP)

For the Moore Threads GPU【https://www.mthreads.com/】, the corresponding FlashMLA version link is as follows: GitHub - MooreThreads/MT-DeepSeek

Citation

@misc{flashmla2025,
      title={FlashMLA: Efficient MLA decoding kernels},
      author={Jiashi Li},
      year={2025},
      publisher = {GitHub},
      howpublished = {\url{https://github.com/deepseek-ai/FlashMLA}},
}