In Depth
Triton Inference Server (formerly TensorRT Inference Server) is an open-source serving platform that can deploy models from virtually any framework: PyTorch, TensorFlow, TensorRT, ONNX, and even custom backends. It provides production-grade serving features including dynamic batching, concurrent model execution, model ensemble pipelines, and GPU/CPU resource management.
Triton's dynamic batching automatically groups incoming requests to maximize GPU utilization, even when requests arrive at irregular intervals. Its model analyzer tool helps find optimal configurations for batch size, instance count, and concurrency. The platform supports model versioning for A/B testing and gradual rollouts, and it provides detailed performance metrics for monitoring.
For enterprises, Triton simplifies the deployment of diverse AI model portfolios. A single Triton deployment can serve computer vision models (TensorRT), language models (PyTorch), and traditional ML models (scikit-learn via custom backends) with consistent APIs and monitoring. It integrates with Kubernetes for scaling and is the serving component of NVIDIA's AI Enterprise platform.