AI applications often suffer a Cold Start problem, where the first user request triggers a long wait. This happens because massive model weights must load from slow disk storage into fast GPU vRAM. The system is functional but idle, like a sports car engine that needs warming before it can perform.

For serverless GPU setups, this involves a three-step bottleneck: Container Spin-up, Weight Transfer from storage like Amazon S3, and CUDA Context initialization. Large models like Llama-3-70B can take 20 seconds to 2 minutes to boot, creating a poor user experience before the AI even generates its first token.

To fix this, engineers use a Warm Pool of always-on instances, though it's costly. Newer tools like NVIDIA's Run:ai Model Streamer enable Model Streaming to load weights faster. Another tactic is Tiered Routing, sending initial queries to a smaller model while the main one boots, ensuring users don't wait.