This article explains Transformer inference, contrasting it with training by introducing latency as a key consideration. It describes how naive token sampling is computationally expensive (O(n²) to O(n³)), but can be optimized using a KV cache to reduce complexity to O(n) to O(n²), enabling efficient sequence generation through separate forward passes for each token.
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