DeepSeek open-sources FlashMLA, breaking through GPU performance limits
DeepSeek open-sources FlashMLA, which revolutionizes GPU performance.
Core content:
1. DeepSeek open-sources FlashMLA, which is deeply optimized for NVIDIA Hopper GPU
2. Core technology MLA architecture significantly improves the efficiency of long-context reasoning
3. Measured performance breakthrough, memory bandwidth and computing performance are close to the hardware limit
MLA architecture (Multi-head Latent Attention) : By transforming the attention mechanism, compressing the KV Cache size and reducing memory usage, it supports longer context processing under the same hardware conditions . The KV Cache of the standard Transformer grows linearly with the sequence length (complexity O(n²)), resulting in memory explosion in long context scenarios. Latent attention compression: The K/V matrix in the multi-head attention is compressed into the latent space through low-rank projection, reducing the KV Cache size by 60%-80% (for example, the original 40GB cache can be compressed to 8-16GB).
Paged KV cache (block size 64) : uses sophisticated memory management strategies to improve cache utilization and reduce latency .
BF16 precision support : takes into account both computing performance and memory efficiency, and adapts to the current mainstream AI hardware requirements .
The measured data on the H800 SXM5 GPU shows
Memory bandwidth : It reaches 3000 GB/s in memory-constrained scenarios, far exceeding the theoretical bandwidth limit of H800 (600 GB/s) and approaching the physical limit of hardware .
Computing performance : 580 TFLOPS is achieved in computing-constrained scenarios, approaching the theoretical peak of the Hopper architecture .
This optimization significantly improves the inference speed of large models, which is especially suitable for real-time generation tasks (such as chatbots and text generation), while reducing deployment costs.
The comparative experimental data released by DeepSeek reveals the significant advantages of FlashMLA
FlashMLA not only significantly reduces costs during the training phase, but also achieves breakthroughs in long-context reasoning scenarios. Its core technologies are:
- Communication optimization: Reduce all-to-all communication bandwidth requirements by 62% through expert gradient compression algorithm
- Computation pipeline reconstruction: overlap the matrix multiplication of the FFN layer with the activation function execution time to improve instruction-level parallelism
- Dynamic load balancing: real-time monitoring of the computing load of each expert, and avoiding idle resources through asynchronous scheduling
