Boost your throughput with dynamic batching

Modal has introduced native dynamic batching to improve throughput and reduce costs for workloads such as machine-learning inference and database queries by grouping incoming requests for shared processing. Unlike fixed-size batching, which can cause long delays during sporadic traffic, dynamic batching processes requests when either a configured maximum batch size is reached or a waiting-time limit expires, allowing users to balance latency and efficiency. The post demonstrates the feature with OpenAI’s Whisper large v3 transcription model on an A10G GPU, where batching avoids repeatedly loading more than six gigabytes of model weights for individual requests. By adding Modal’s `@modal.batched()` decorator and adapting the inference function to accept and return lists, the example achieved throughput of roughly 3.3 requests per second instead of 1.2, a 2.8-fold improvement that reduced inference costs by about 65 percent.

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