Reading
Vortex is a programmable inference system specifically designed for sparse attention algorithms. Through a Python-embedded front-end language and page-centric tensor abstraction, it enables rapid prototyping and large-scale deployment of sparse attention algorithms, achieving up to a 4.7x throughput improvement on models like GLM-4 and MiniMax-M2.
Section 01
Vortex is a programmable inference system designed specifically for sparse attention algorithms. It bridges rapid prototyping and large-scale deployment via a Python-embedded front-end language and page-centric tensor abstraction, achieving up to 4.7x throughput improvement on GLM-4 models and 1.37x on MiniMax-M2, supporting both research innovation and AI agent-driven exploration.
Section 02
As LLM context lengths grow to hundreds of thousands of tokens, standard attention's O(n²) complexity leads to explosive computational costs. Sparse attention (sliding window, local-global hybrid) reduces complexity but faces deployment challenges—converting theoretical algorithms to efficient implementations requires extensive engineering work, slowing innovation and AI agent exploration.
Section 03
Vortex's architecture optimizes sparse attention deployment:
Section 04
Sparse attention's vast design space is impractical for manual exploration. Vortex's concise front-end enables AI agents to auto-generate and evaluate variants. Experiments show agents using Vortex discovered algorithms with up to 3.46x throughput gain over full attention while maintaining accuracy.
Section 05
Vortex's performance is verified across models:
Section 06
Application Value:
Limitations & Future Work:
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