How LLM Inference Works: Tokenization, GPU Batching, and Real-Time Response Generation

This video breaks down LLM inference—the process that transforms a user prompt into an AI model's response. It walks through tokenization (converting text to numbers the model understands), GPU batching (processing multiple requests at once), KV cache (storing key-value pairs to speed up generation), and the prefill and decode phases that generate tokens one at a time.

The explainer covers how inference engines route requests to hardware, measure performance with metrics like time-to-first-token and inter-token latency, and connect all these pieces into a working pipeline. It's aimed at engineers and technical professionals building or deploying AI applications.