Using a Transformer Model: From Training to Inference

The chapter explains how a decoder‑only transformer performs autoregressive generation by predicting each token from its predecessors. It introduces a prefill‑and‑decode workflow, a lightweight key‑value (KV) cache that stores intermediate representations, and discusses the memory footprint of that cache during inference. The discussion highlights that a simple KV cache can reduce repeated computation, but also notes the trade‑off between cache size and memory consumption.

⚡ Key Takeaways

• The chapter is structured around four core concepts: autoregressive generation, prefill and decode, a simple KV cache, and memory usage of the KV cache.
• It details how a decoder‑only transformer predicts the next token based solely on preceding tokens.
• The prefill‑and‑decode strategy is presented as a way to handle long contexts efficiently.
• A simple KV cache design is shown to store key/value pairs per token, enabling faster inference by reusing cached representations.
• The memory usage of the KV cache is analyzed, illustrating the trade‑off between cache size and available GPU memory.
• Why

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