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FlashMLA：DeepSeek开源的高效MLA解码内核，专为NVIDIA Hopper GPU优化

FlashMLA：DeepSeek开源的高效MLA解码内核，专为NVIDIA Hopper GPU优化

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FlashMLA is an open-source, high-performance Multi-Head Linear Attention (MLA) decoding kernel optimized for NVIDIA Hopper architecture GPUs, designed to handle variable-length sequences efficiently. It enhances memory and computational efficiency through optimized KV caching and BF16 data format support, achieving up to 3000 GB/s memory bandwidth and 580 TFLOPS computational performance on H800 SXM5 GPUs. FlashMLA is ideal for large language model (LLM) inference and natural language processing (NLP) tasks requiring efficient decoding. (FlashMLA是DeepSeek开源的高效MLA解码内核，专为NVIDIA Hopper架构GPU优化，用于处理可变长度序列。通过优化KV缓存和采用BF16数据格式，提升了内存和计算效率，在H800 SXM5 GPU上内存带宽可达3000 GB/s，计算性能可达580 TFLOPS。适用于大语言模型推理和需要高效解码的自然语言处理任务。)
AI 搜索观察2026/1/23
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FlashMLA：DeepSeek高性能注意力内核库，驱动V3模型实现660 TFLOPS

FlashMLA：DeepSeek高性能注意力内核库，驱动V3模型实现660 TFLOPS

BLUF
FlashMLA is DeepSeek's optimized attention kernel library that powers DeepSeek-V3 models, featuring token-level sparse attention with FP8 KV cache support, achieving up to 660 TFLOPS performance on NVIDIA H800 GPUs. (FlashMLA是DeepSeek优化的注意力内核库，为DeepSeek-V3模型提供动力，具有令牌级稀疏注意力和FP8 KV缓存支持，在NVIDIA H800 GPU上实现高达660 TFLOPS的性能。)
AI 搜索观察2026/1/23
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