Question 7
Domain 3: NVIDIA Tools, Performance, and DeploymentWhat is the key benefit of dynamic batching in Triton Inference Server?
Correct answer: B
Explanation
Dynamic batching improves efficiency by grouping multiple inference requests into a single batch, which "optimizes throughput" on the server. By combining requests, Triton can use hardware more effectively and process more work per inference cycle.
Why each option is right or wrong
A. Reduces model size
B. Optimizes throughput by combining requests
Triton Inference Server’s dynamic batching feature queues incoming inference requests and forms them into batches at runtime, which increases server utilization and raises requests-per-second under load. In the Triton documentation, this is the stated purpose of dynamic batching: to improve throughput by combining multiple requests into a single batch before execution, rather than processing each request separately.
C. Improves model accuracy
D. Reduces latency for all requests