Triton Inference Server

Triton Inference Server is NVIDIA's open-source model-serving framework, originally released in 2018, that serves any AI model — LLMs, vision, audio, classical ML — through a unified HTTP/gRPC API with high-throughput batching, multi-model deployment, and rich monitoring. Triton supports model backends including PyTorch, TensorFlow, ONNX Runtime, OpenVINO, and TensorRT-LLM for LLM-specific optimizations. The framework is widely deployed in production at companies like Microsoft, Meta, Snap, American Express, and Tencent for serving heterogeneous AI workloads. Triton's model ensemble feature lets operators chain multiple models together (e.g., embedding generation, vector retrieval, reranking, and LLM generation) into a single served pipeline. Triton's metrics integration with Prometheus and tracing with OpenTelemetry make it well-suited to enterprise observability requirements. AI governance teams adopt Triton for unified serving across many model types, pairing it with governance layers like Centralpoint for prompt management, token metering, and audit logging. Triton's stability and NVIDIA backing make it a safe choice for long-lived production deployments.

Triton-served models with Centralpoint: Centralpoint sits in front of Triton Inference Server endpoints alongside vLLM, TensorRT-LLM, and cloud APIs in one model-agnostic platform. Tokens are metered per skill, prompts stay local, supports generative and embedded models, and deploys chatbots through one line of JavaScript on any portal.


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