Inference

Batch Inference

Batch inference processes multiple requests together, improving GPU utilization and throughput. Dynamic batching collects requests and processes them together. Batching increases latency (requests wait to be batched) but improves overall throughput. Batch size is a tuning parameter affecting latency/throughput trade-off. Large batches maximize throughput but hurt individual request latency. Optimal batch size depends on hardware and use case. Batching is essential for efficient inference at scale.