NVIDIA TensorRT is the component primarily responsible for optimizing deep learning inference

performance by leveraging NVIDIA GPU architecture (e.g., Tensor Cores on A100 GPUs). TensorRT

optimizes trained models through techniques like layer fusion, precision reduction (e.g., FP16, INT8),

and kernel tuning, delivering low-latency, high-throughput inference. It’s tailored for production

environments, as detailed in NVIDIA’s "TensorRT Developer Guide," making it distinct from other

stack components.

cuDNN (A) provides neural network primitives for training and inference but lacks TensorRT’s

optimization depth. Triton Inference Server (C) deploys models efficiently but relies on TensorRT for

optimization. CUDA Toolkit (D) is a foundational platform, not specific to inference optimization.

TensorRT is NVIDIA’s core inference optimizer.

Reference:TensorRT Developer Guide, AI Infrastructure and Operations Fundamentals

(www.nvidia.com).