FlashMLA-ETAP: Efficient Transpose Attention Pipeline for Accelerating MLA Inference on NVIDIA H20 GPUs
arXiv:2506.01969v3 Announce Type: replace-cross Abstract: Efficient inference of Multi-Head Latent Attention (MLA) is challenged by deploying the DeepSeek-R1 671B model on a single Multi-GPU server. This paper introduces FlashMLA-ETAP, a novel framework that enhances MLA inference for the single-instance deployment scenario on NVIDIA H20 GPUs. We propose the Efficient Transpose Attention Pipeline (ETAP), which reconfigures attention computation through transposition to align the KV context length with the \(M\)-dimension in WGMMA operations, significantly reducing redundant computations. Flash
The rapid scaling of large language models like DeepSeek-R1 671B is pushing the boundaries of current GPU inference capabilities, necessitating immediate optimization breakthroughs.
Efficient and scalable AI inference is critical for deploying advanced models economically, directly impacting the accessibility and practical application of frontier AI.
This advancement introduces a new method to significantly reduce computational redundancy for multi-head latent attention, making large-scale model inference more efficient on existing hardware.
- · NVIDIA
- · Hyperscale cloud providers
- · AI model developers
- · Users of large AI models
- · Developers of less efficient inference solutions
- · Competitors without similar optimization capabilities
FlashMLA-ETAP directly enables more cost-effective deployment of massive AI models on single-server, multi-GPU configurations.
Increased inference efficiency will accelerate the commercialization and broader adoption of AI agents and complex AI applications.
More efficient AI deployments could intensify the demand for high-performance GPUs and potentially shift the competitive landscape for AI hardware and software solutions.
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Read at arXiv cs.LG