LLM Batching Explained: Static vs Dynamic vs Continuous Batching

TL;DR

Batching in AI, particularly for large language models (LLMs), can significantly enhance GPU efficiency by processing multiple requests simultaneously. Three approaches—static, dynamic, and continuous batching—each have unique benefits and limitations in optimizing resource use and response times.

Key Takeaways

• Implement dynamic batching to reduce wait times under light traffic.
• Use continuous batching for real-time applications to maximize GPU utilization.
• Monitor output lengths to choose the best batching strategy for your use case.
• Consider memory limits when selecting batch sizes for LLMs.