Continuous vs dynamic batching for AI inference

Batching makes good use of GPU resources by processing multiple requests to an AI model simultaneously, but choosing the right batching strategy depends on the model architecture and modality. For most LLM deployments, continuous batching maximizes throughput by processing requests token-by-token, while dynamic batching is suitable for other generative models where each output takes a similar amount of time to create. Continuous batching offers even better performance for LLMs due to its ability to optimize next token prediction, but requires careful configuration based on traffic patterns and latency requirements. By selecting the right batching strategy, developers can maximize GPU utilization and hit ambitious latency targets while serving AI models in production.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.