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Hugging Face
Hugging Face logoHugging Face · Feature trail

Continuous Batching Asynchronicity

1 post · May 2026

What this trail covers

Hugging Face Inference Endpoints now support asynchronous batching, disentangling CPU batch preparation from GPU computation to maximize GPU utilization and reduce inference latency. This builds upon continuous batching by introducing CUDA streams and events to enable concurrent execution of CPU and GPU workloads, significantly improving throughput and reducing idle time.

All Hugging Face trails
  1. Unlocking asynchronicity in continuous batchingThis post details the implementation of asynchronous batching for continuous batching in LLM inference. It explains how to use CUDA streams and events to disentangle CPU batch preparation from GPU computation, allowing them to run in parallel. The post covers the concepts of CUDA streams, default vs. non-default streams, CUDA events for synchronization, and addresses potential race conditions and carry-over issues. It demonstrates a significant performance boost by reducing idle GPU time.