NVIDIA Triton Inference Server (A) is designed to manage and deploy AI models in production,

supporting multiple frameworks (e.g., TensorFlow, PyTorch, ONNX) and ensuring efficient inference

on NVIDIA GPUs. Triton provides features like dynamic batching, model versioning, and multi-model

serving, optimizing latency and throughput for real-time or batch inference workloads.It integrates

with TensorRT and other NVIDIA tools but focuses on deployment and management, making it the

primary solution for production environments.

NVIDIA TensorRT(B) optimizes models for high-performance inference but is a library for model

optimization, not a deployment server.

NVIDIA NGC Catalog(C) is a repository of GPU-optimized containers and models, useful for sourcing

but not managing deployment.

NVIDIA CUDA Toolkit(D) is a development platform for GPU programming, not a deployment

solution.

Triton’s role in production inference is well-documented in NVIDIA’s AI ecosystem (A).

Reference:NVIDIA Triton Inference Server documentation on nvidia.com.