Part 8: Inference Optimization & vLLM Deployment on Production
1. The LLM Bottleneck: Why Are GPUs Still Idle? After finishing designing the entire Agent architecture in the previous 7 parts, it is time to push your system to Production (live running). Every startup soon realizes a bitter truth: The enemy of LLMs is not Compute Power, but Memory Bandwidth. To run the Llama-3 70B model (standard FP16), you need about 140GB of VRAM just to hold the model weights. But when 100 Users send prompts simultaneously, the system must generate a temporary memory space called the KV Cache to retain the context of those 100 conversations. Instantly, the KV Cache bloats and drains all remaining VRAM. The system throws an Out-Of-Memory (OOM) error and crashes, even though the GPU’s processing power was only 30% utilized. How do you “cram” more Users into the GPU without overflowing RAM? ...