The source analyzes Large Language Model (LLM) inference, specifically focusing on how continuous batching significantly improves efficiency compared to traditional static batching. It explains the inefficiencies of static batching where GPUs are underutilized due to varying output lengths in a batch, and introduces continuous batching (also known as dynamic batching or iteration-level scheduling) as a solution that dynamically adds new requests as others complete. The document further highlights PagedAttention and vLLM as advanced memory optimization techniques built upon continuous batching, leading to even greater throughput and reduced latency. Benchmarking results demonstrate how these innovations drastically enhance throughput and lower latency across different workloads, ultimately reducing the cost of serving LLMs.
Source: https://www.anyscale.com/blog/continuous-batching-llm-inference
