Serving YOLOv8 with NVIDIA Triton via ONNX and TensorRT

This article details how to serve the YOLOv8 object detection model using NVIDIA Triton Inference Server. It explains the process of converting the ONNX format of YOLOv8 to TensorRT, a high-performance inference optimizer, and then deploying it via Triton's native TensorRT backend. The guide specifically highlights the use of Triton Control to streamline the creation of a target-specific TensorRT plan for efficient model deployment. AI

IMPACT Provides a technical guide for optimizing and deploying computer vision models, potentially improving inference speed and efficiency for AI applications.

RANK_REASON The article describes a technical process for deploying an existing model (YOLOv8) using specific inference server software (NVIDIA Triton) and optimization libraries (ONNX, TensorRT). This falls under tooling and implementation rather than a new release or significant industry event.

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• NVIDIA Triton
• ONNX
• TensorRT
• Triton Control
• YOLOv8

• infra
• product

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