Scale Model Serving with Dynamic Batching and Autoscaling

Dynamic batching and autoscaling can substantially improve production serving capacity for Stable Diffusion by combining incoming requests into parallel GPU batches and adding or removing model servers according to demand. In the described deployment, batching requests up to 12 at a time increased throughput by 50% and raised per-process concurrency from about 30 to 60 users, with the optimal batch size depending on available GPU memory. Requests were queued, processed by a background coroutine, and returned through a results mechanism, while parallel model servers ran across multiple GPUs. An autoscaling job evaluated queue traffic every 30 seconds, maintaining a configurable minimum server count and scaling capacity as needed; increasing from one to four servers raised concurrency from 60 to roughly 400 users. Performance testing with Locust showed that four servers combined with batches of 12 increased supported concurrent users more than tenfold and could be extended to support thousands of requests.

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