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This article explains how Large Language Models (LLMs) process prompts from tokenization to response generation. It covers the transformer architecture, including self-attention and feed-forward networks, and details the importance of the KV cache in optimizing performance.
This article explains how prompt caching works in large language models, focusing on techniques like paged attention and KV-cache reuse. It offers practical tips for improving cache hits to enhance performance and reduce costs in API usage.