Vortex: Efficient and Programmable Sparse Attention Serving for AI Agents
- Published
- Source
- arXiv
- Paper number
- 344
- Field
- AI / General
- arXiv ID
- 2606.06453
Key points
- A Python-embedded frontend allows rapid prototyping of sparse attention algorithms.
- AI agents automatically generate and refine algorithms to achieve a 3.46x throughput improvement.
- It accelerates GLM-4.7-Flash MLA by 4.7x and MiniMax-M2.7 229B by 1.37x.
- It provides an efficient backend tightly integrated with modern LLM serving stacks.
- It expands the applicability of sparse attention to emerging architectures and ultra-large models.
Paper links
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