LLM Inference Optimization: How Continuous Batching Works

Text generation in an LLM is a process of predicting tokens one by one, and each token incurs a massive computational cost. This analyzes the operating principles of Continuous Batching, a core technology for solving this problem and efficiently serving many users at once, starting from the attention mechanism and basic computation steps.

Key Points:

• Attention Mechanism: The core component that enables interactions between tokens, with computation that scales quadratically with sequence length.
• Nature of Computation: Explains the difference between token-wise operations such as layer normalization and operations like attention that compute relationships between tokens.
• Role of Continuous Batching: Optimizes the system's throughput by processing multiple conversations in parallel and replacing each conversation with a new request as soon as it completes.