Continuous Batching: Optimizing LLM Inference Throughput › Summary

Continuous batching turns LLM inference from rigid, inefficient batch processing into a dynamic, high-utilization system. The main ideas we covered include:

• Static batching inefficiency. Traditional batching forces all requests to wait for the slowest, wasting compute on completed requests and inflating latencies. Efficiency can drop to 20% or below for workloads with high output length variance.
• Iteration-level scheduling. Treating each decode step as a scheduling opportunity allows immediate eviction of completed requests and admission of waiting ones. The batch composition changes continuously rather than at fixed batch boundaries.
• Phase handling. Managing the distinct prefill and decode phases, potentially with chunked prefill, balances compute characteristics across the batch and prevents long prompts from monopolizing the GPU.
• Resource management. Tight integration with KV cache memory management, including paged attention, maximizes the number of concurrent requests by returning memory to the pool immediately on completion.
• Theoretical efficiency. Continuous batching approaches 100% compute efficiency because total capacity used matches total useful work, independent of output length variance.
• Throughput gains. Real-world improvements of 2-5x throughput are common, with better latency characteristics especially for short requests.

Continuous batching is now standard practice for production LLM serving. Combined with the memory optimizations from paged attention and the speculative decoding techniques we covered earlier, it lets serving LLMs at scale with reasonable hardware costs.