Triton Inference Server

Triton Inference Server is an open-source inference serving software developed by NVIDIA that enables the deployment of AI models from any framework (e.g., TensorFlow, PyTorch, ONNX) on GPU or CPU infrastructure. It provides a unified platform for model serving with features like dynamic batching, concurrent model execution, and model ensemble pipelines. It is designed for high-performance, scalable inference in production environments, particularly optimized for NVIDIA GPUs.

Also known as: NVIDIA Triton, Triton Server, Triton, Triton Inference, TIS

Why learn Triton Inference Server?

Developers should use Triton Inference Server when deploying machine learning models in production at scale, especially in GPU-accelerated environments, as it reduces latency and increases throughput through optimizations like dynamic batching and concurrent execution. It is ideal for applications requiring real-time inference, such as autonomous vehicles, recommendation systems, or natural language processing services, where low latency and high availability are critical. It also simplifies model management by supporting multiple frameworks and enabling easy updates without downtime.

Learning Resources

• docs: Triton Inference Server Documentation
• tutorial: NVIDIA Triton Inference Server Tutorial
• course: Deploying Models with Triton on Coursera
• video: Triton Inference Server Overview Video