What is adaptive batching in high-throughput ML inference systems?

Adaptive batching dynamically groups inference requests based on system load and latency constraints.

Adaptive batching adjusts batch sizes in real time depending on traffic patterns and latency budgets. Unlike static batching, it balances throughput and latency dynamically. It is commonly used in GPU inference servers like NVIDIA Triton. The system must carefully tune timeout windows to avoid excessive queuing delays.