Understanding Continuous Batching in Large Language Models: From Attention Mechanisms to Throughput Optimization
By
jxmorris12
3mo ago· 18 min readenInsight
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Summary
This technical blog post explains continuous batching in large language models (LLMs) by starting from first principles of attention mechanisms and KV caching. The article demonstrates how continuous batching optimizes throughput by allowing multiple requests to be processed simultaneously, addressing the inefficiency of traditional sequential processing where LLMs generate tokens one at a time. The author walks through the mathematical and computational foundations, showing how continuous batching enables more efficient GPU utilization and faster response times in AI chatbots like Qwen and Claude.
Key quotes
· 4 pulledIf you've ever used Qwen, Claude, or any other AI chatbot, you've probably noticed something: it takes a while for the first word of the response to appear, and then words appear one-by-one on your screen with (hopefully) a regular and fast-paced frequency.
At the heart of it, all LLMs are just fancy next token predictors. An LLM first processes your entire prompt to produce one new token.
Continuous batching allows multiple requests to be processed simultaneously, optimizing for throughput by addressing the inefficiency of traditional sequential processing.
Starting from attention mechanisms and KV caching, we derive continuous batching by optimizing for throughput.
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