DeepSeek's FlashMLA: Tunable Performance Parameters
DeepSeek's FlashMLA introduces tunable performance parameters that allow developers to optimize multi-head latent attention mechanisms by adjusting
Someone found DeepSeek’s FlashMLA implementation on GitHub with some interesting performance parameters worth tweaking.
The main interface at https://github.com/deepseek-ai/FlashMLA/blob/main/flash_mla/flash_mla_interface.py exposes tunable options like:
block_size_k=128,
num_warps=4,
num_stages=2Turns out adjusting num_warps (typically 4 or 8) and num_stages (pipeline depth) can significantly impact throughput depending on GPU architecture. The block_size parameters control memory tiling - smaller blocks reduce memory usage but might hurt speed.
For A100/H100 GPUs, bumping num_warps=8 and num_stages=3 often gives better performance. For older hardware, sticking with defaults is safer.
Pretty useful for anyone running custom DeepSeek deployments and trying to squeeze out extra performance without rewriting kernels from scratch.
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