A Guide to AI Inference Engineering

Running AI models in production efficiently requires understanding a fundamental split: the "prefill" phase processes input prompts and is limited by raw computing power, while the "decode" phase generates each response token sequentially and is limited by memory speed. Inference engineers use techniques like batching, prefix caching, quantization, and disaggregation to optimize both phases, cutting costs by roughly 80% compared to closed APIs while improving speed and reliability.