STREAM2LLM System Reduces AI Inference Latency Through Context Streaming

STREAM2LLM is a novel system designed to tackle latency issues in large language model inference by integrating context retrieval with processing. It enhances the vLLM framework to accommodate streaming prompts, featuring adaptive scheduling and preemption. The system effectively manages two retrieval patterns: append-mode for gradual context accumulation and update-mode for iterative refinement with cache invalidation. By separating scheduling from resource acquisition, STREAM2LLM facilitates flexible preemption strategies based on hardware-specific cost models. This research confronts the inherent dilemma in context retrieval systems, where high retrieval latency forces a choice between waiting for complete context or proceeding without it. Unlike previous systems that focused on single-request scenarios, this architecture efficiently manages concurrent requests in multi-tenant environments, addressing GPU memory contention and adapting to dynamic context arrivals.