WarmServe: Enabling One-for-Many GPU Prewarming for Multi-LLM Serving
Researchers have developed WarmServe, a new system designed to improve the efficiency of serving multiple large language models (LLMs) on shared GPU clusters. WarmServe utilizes a one-for-many GPU prewarming strategy, proactively loading model parameters based on predicted workload patterns. This approach aims to reduce the time-to-first-token (TTFT) degradation often seen in multi-LLM serving systems. Evaluations indicate WarmServe can significantly decrease tail TTFT and increase request throughput compared to existing methods. AI
IMPACT Optimizes LLM serving infrastructure, potentially reducing latency and increasing throughput for deployed models.