Foundations

LLM inference is where models meet the real world. It powers everything from instant chat replies to code generation, and directly impacts latency, cost, and user experience. Understanding how inference works is the first step toward building smarter, faster, and more reliable AI applications.

📄️ What is LLM inference?

LLM inference is the process of using a trained language model to generate responses or predictions based on prompts.

📄️ Training vs. inference

LLM training builds the model while LLM inference applies it to generate real-time outputs from new inputs.

📄️ How does LLM inference work?

Learn how LLM inference works, from tokenization to prefill and decode stages, with tips on performance, KV caching, and optimization strategies.

📄️ Where is LLM inference run?

Learn the differences between CPUs, GPUs, and TPUs and where you can deploy them.

📄️ Key metrics for LLM inference

Measure key metrics like latency and throughput to optimize LLM inference performance.

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