稀疏注意力是长上下文 LLM 服务的关键瓶颈，Vortex 让 AI Agent 和研究者能快速实验新算法，做 LLM 推理优化的团队可以直接用它提升吞吐量，值得关注。
Vortex 是一个专为大规模语言模型（LLM）设计的稀疏注意力服务系统，解决了稀疏注意力算法在部署和评估中工程成本高的问题。它通过 Python 嵌入式前端语言和页面中心张量抽象，支持广泛稀疏注意力算法的快速原型设计、部署和评估。在 NVIDIA B200 GPU 上，Vortex 使 AI Agent 自动生成的算法吞吐量比全注意力提升高达 3.46 倍，并在 MLA 架构的 GLM-4.7-Flash 和 229B 参数的 MiniMax-M2.7 上分别实现 4.7 倍和 1.37 倍的吞吐量提升。该系统显著加速了稀疏注意力算法的迭代，尤其适用于长序列生成场景。
Vortex: Efficient and Programmable Sparse Attention Serving for AI Agents
Sparse attention is becoming increasingly important for serving large language models (LLMs) as generation lengths continue to grow. However, deploying and evaluating new sparse attention algorithms at scale remains highly engineering-intensive, slowing both human researchers and AI agents in exploring the sparse attention design. To address this challenge, we present Vortex, a system that combines a Python-embedded frontend language atop a page-centric tensor abstraction for expressing a broad range of sparse attention algorithms, with an efficient backend tightly integrated into modern LLM serving stacks. Vortex enables rapid prototyping, deployment, and evaluation of sparse attention algorithms, effectively translating their theoretical efficiency gains into real-world throughput improvements. As a result, Vortex substantially accelerates the design and iteration of sparse attention algorithms. First, AI agents use Vortex to automatically generate and refine diverse algorithms, the best reaching up to $3.46\times$ higher throughput than full attention while preserving accuracy. Second, Vortex extends sparse attention to emerging architectures and very large models that are otherwise hard to experiment with, reaching up to $4.7\times$ higher throughput on the MLA-based GLM-4.7-Flash and $1.37\times$ on the 229B-parameter MiniMax-M2.7 on NVIDIA B200 GPUs.