Hugging Face details asynchronous batching technique to reduce GPU idle time in continuous inference

Hugging Face published a technical guide explaining asynchronous batching, a method to improve GPU utilization in continuous inference loops. The approach decouples CPU batch preparation from GPU computation, allowing both to run in parallel using CUDA streams rather than the synchronous alternation that characterizes typical batching.

Traditional synchronous batching follows a turn-taking pattern: the CPU prepares a batch (selects requests, updates key-value cache, schedules new inputs), sends work to the GPU, then waits idle while the GPU computes. Once the GPU finishes, the CPU resumes to process outputs and prepare the next batch, leaving the GPU idle. The post demonstrates this inefficiency through a concrete profile: generating 8,000 tokens with batch size 32 on an 8B model took 300.6 seconds total, with 24% of that duration spent with idle GPU.

To achieve concurrent execution, the approach leverages CUDA streams—independent ordered queues of GPU operations that can run concurrently across different streams. Operations within a single stream execute sequentially; operations across streams can overlap. By enqueuing GPU work on non-default streams, the CPU regains control immediately after the launch and can proceed to prepare the next batch while prior GPU work runs in the background, eliminating synchronization barriers.

The post walks through CUDA stream mechanics, explaining that PyTorch's default stream is inherently synchronizing—it blocks all other work until it completes—which would defeat asynchronous goals. Non-default streams return control to the CPU immediately, enabling true concurrent CPU and GPU activity. The transformers library now includes a working implementation of asynchronous continuous batching with no new model kernels required.