server: an open-source inference server that streamlines AI model deployment across multiple frameworks and hardware platforms

What it solves

Triton Inference Server streamlines the deployment of AI models by providing a standardized way to serve them across various hardware platforms (cloud, data center, edge, and embedded devices) and frameworks. It eliminates the need to write custom serving infrastructure for every different model type or framework used in a production environment.

How it works

Triton acts as a serving layer that supports multiple backends (such as TensorRT, PyTorch, ONNX, OpenVINO, and Python). It allows users to place models into a model repository and configure them for optimized performance. It provides HTTP/REST and gRPC protocols for clients to send inference requests and receive responses, and it can be linked directly into applications via C and Java APIs for in-process use cases.

Who it’s for

It is designed for AI teams and developers who need to deploy production-grade AI models at scale, supporting a wide range of hardware (NVIDIA GPUs, x86/ARM CPUs, AWS Inferentia) and multiple deep learning frameworks.

Highlights

• Multi-framework support: Serves models from TensorRT, PyTorch, ONNX, OpenVINO, Python, and RAPIDS FIL.
• Optimized performance: Features dynamic batching, sequence batching, and concurrent model execution to maximize throughput and minimize latency.
• Flexible deployment: Supports cloud, data center, edge, and embedded devices.
• Extensible architecture: Provides a Backend API for adding custom backends and pre/post processing operations.
• Model pipelining: Enables complex workflows using Ensembling or Business Logic Scripting (BLS).
• Integrated metrics: Provides built-in metrics for GPU utilization, server throughput, and latency.