Inference Batching: Grouping Requests for Throughput
Think of inference batching as a carpool for your ML model. Instead of sending each request in its own car, you wait a few microseconds to fill a bus, dramatically improving GPU efficiency.
Inference batching is like a carpool for your ML model. Instead of processing each request individually, the server waits a few microseconds to group multiple requests into a single, larger batch. This dramatically improves GPU utilization and overall throughput in high-traffic systems like LLM APIs, trading a tiny bit of latency for a big boost in efficiency. The key footgun is tuning the batching delay: too long increases latency, while too short negates the throughput benefit.
Read the original → docs.nvidia.com
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