Fast Warm-Start Techniques for Large Language Model Inference

Large language model (LLM) inference servers experience significant latency during cold starts—the period from process initialization to serving the first request. We measure cold-start time-to-first-token (TTFT) exceeding 45 seconds for 7B-class models and 80 seconds for 13B-class models on commodity GPUs. This latency creates SLA violations during scaling events, deployments, and fault recovery. We present a system of engineering techniques that reduce cold-start overhead without modifying model weights or architecture: artifact pre-staging, CUDA context pre-initialization, memory allocator priming, and kernel warm-up. Our evaluation on Paperspace GPU instances shows TTFT reductions of 42–68% (7B models) and 31–54% (13B models) compared to naïve cold starts, while maintaining steady-state throughput within 3% of baseline. We characterize the breakdown of cold-start phases and demonstrate that systematic warm-start optimization is practical for production serving systems.