LLM Batching: Static vs Continuous and Why It Matters for Throughput

The text discusses the inefficiencies of static and dynamic batching in GPU processing and introduces continuous batching as a more efficient alternative, particularly for real-time applications with variable output lengths. Static batching, which waits for all requests in a batch to complete, leads to idle GPU cycles and wasted resources when dealing with requests of varying lengths. Dynamic batching improves slightly by allowing batches to start based on size or time, but still requires all requests to finish before moving on. Continuous batching, as implemented in vLLM and other frameworks, allows requests to join and leave the batch independently, eliminating padding and maintaining high throughput regardless of output variance. This method, combined with PagedAttention to optimize memory usage, dramatically enhances performance, achieving up to 23 times the throughput of traditional models and doubling the efficiency of existing continuous batching methods. This approach is particularly beneficial for applications like chatbots and interactive AI, where latency and throughput are critical.