Sparse attention is essential for optimizing large language models (LLMs) as the demand for longer generation lengths increases. To streamline the deployment and evaluation of sparse attention algorithms, researchers have developed Vortex, a system that integrates a Python-embedded frontend with a page-centric tensor abstraction. This combination facilitates the expression and rapid prototyping of various sparse attention algorithms while enhancing integration with existing LLM serving stacks. Vortex allows AI agents to autonomously generate and refine algorithms, achieving up to 3.46 times higher throughput compared to full attention without sacrificing accuracy. Additionally, Vortex has demonstrated improvements of up to 4.7 times throughput for the MLA-based GLM-4.7-Flash and 1.37 times for the 229B-parameter MiniMax-M2.7 models on NVIDIA B200 GPUs, marking significant advancements in the efficiency of AI model serving.
Introducing Vortex: A Breakthrough in Sparse Attention for AI Agents
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