Using a Transformer Model: From Training to Inference

A deep dive into how transformer models differ between training and inference, focusing on autoregressive generation, the prefill/decode split, and KV caching. Explains why naive inference is inefficient due to repeated recomputation, then walks through implementing a simple KV cache in PyTorch. Covers memory usage calculations for the KV cache and discusses production considerations like pre-allocation, paged layouts, and why cache management is a key differentiator in inference systems.

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