Technology
DeepSeek Open Source FlashMLA – MLA Decoding Kernel for Hopper GPUs
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FlashMLA
FlashMLA is an efficient MLA decoding kernel for Hopper GPUs, optimized for variable-length sequences serving.
Currently released:
- BF16
- Paged kvcache with block size of 64
Quick start
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 12.6.
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
- PyTorch 2.0 and above
Acknowledgement
FlashMLA is inspired by FlashAttention 2&3 and cutlass projects.
Citation
@misc{flashmla2025, title={FlashMLA: Efficient MLA decoding kernel}, author={Jiashi Li}, year={2025}, publisher = {GitHub}, howpublished = {url{https://github.com/deepseek-ai/FlashMLA}}, }