Continuous batching is an optimization technique aimed at enhancing the throughput of large language models (LLMs) by processing multiple conversations in parallel without unnecessary computational overhead. This approach builds on several key components: attention mechanisms, KV caching, chunked prefill, ragged batching, and dynamic scheduling. The attention mechanism allows for efficient token interaction, while KV caching reduces computation by storing previously calculated states for reuse. Chunked prefill manages large initial prompts by splitting them into smaller, manageable chunks to fit within memory constraints. Ragged batching eliminates padding waste by concatenating prompts and using attention masks to control token interaction, thus maximizing memory usage. Dynamic scheduling further optimizes throughput by swapping completed prompts with new ones, ensuring continuous and efficient resource utilization. These techniques collectively enable modern LLMs to serve multiple users concurrently, exemplified by services like ChatGPT.