NVIDIA Triton Inference Server
NVIDIA Triton™ Inference Server is an open-source inference serving software that helps standardize model deployment and execution and delivers fast and scalable AI in production.
Get StartedWhat Is NVIDIA Triton?
Triton Inference Server streamlines AI inference by enabling teams to deploy, run and scale trained AI models from any framework on any GPU- or CPU-based infrastructure. It provides AI researchers and data scientists the freedom to choose the right framework for their projects without impacting production deployment. It also helps developers deliver high-performance inference across cloud, on-prem, edge, and embedded devices.
Support for Multiple Frameworks
Triton Inference Server supports all major frameworks, such asTensorFlow, NVIDIA® TensorRT™, PyTorch, MXNet, Python, ONNX, RAPIDS™ FIL (for XGBoost, scikit-learn, etc.), OpenVINO, custom C++, and more.
High Performance Inference
Triton supports all NVIDIA GPU-, x86-, and ARM® CPU-based inferencing. It offers features like dynamic batching, concurrent execution, optimal model configuration, model ensemble, and streaming inputs to maximize throughput and utilization.
Designed for DevOps and MLOps
Triton integrates with Kubernetes for orchestration and scaling, exports Prometheus metrics for monitoring, supports live model updates, and can be used in all major public cloud AI and Kubernetes platforms. It’s also integrated in many MLOPS software solutions.
Fast-Track Your AI Journey with NVIDIA LaunchPad
Experience Triton Inference Server and other NVIDIA AI software through free curated labs on hosted infrastructure.
Ecosystem Integrations with NVIDIA Triton
AI is driving innovation across businesses of every size and scale. An open-source software solution, Triton is the top choice for AI inference and model deployment. Triton is supported by Alibaba Cloud, Amazon Elastic Kubernetes Service (EKS), Amazon Elastic Container Service (ECS), Amazon SageMaker, Google Kubernetes Engine (GKE), Google Vertex AI, HPE Ezmeral, Microsoft Azure Kubernetes Service (AKS), and Azure Machine Learning. Discover why enterprises use Triton.
NVIDIA AI Inference Resources
Simplify AI Deployment at Scale
Simplify the deployment of AI models at scale in production. Learn how Triton meets the challenges of deploying AI models and review the steps to get started.
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Deploy AI Deep Learning Models
Get the latest news and updates, and learn more about key benefits on the NVIDIA Technical Blog.
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Read Product Documentation
See what’s new and find out more about the latest features in the Triton release notes.
Read on GitHubFast and Scalable AI In Every Application
NVIDIA Triton Inference Server delivers high inference throughput:
Triton executes multiple models from the same or different frameworks concurrently on a single GPU or CPU. In a multi-GPU server, Triton automatically creates an instance of each model on each GPU to increase utilization.
It also optimizes serving for real-time inferencing under strict latency constraints, supports batch inferencing to maximize GPU and CPU utilization, and streaming inference with built-in support for audio and video streaming input. Triton supports model ensemble for use cases that require multiple models to perform end-to-end inference, such as conversational AI.
Models can be updated live in production without restarting Triton or the application. Triton enables multi-GPU, multi-node inference on very large models that cannot fit in a single GPU memory.
NVIDIA Triton Inference Server delivers highly scalable inference:
Also available as a Docker container, Triton integrates with Kubernetes for orchestration, metrics, and autoscaling. Triton also integrates with Kubeflow and Kubeflow pipelines for an end-to-end AI workflow and exports Prometheus metrics for monitoring GPU utilization, latency, memory usage, and inference throughput. It supports the standard HTTP/gRPC interface to connect with other applications like load balancers and can easily scale to any number of servers to handle increasing inference loads for any model.
Triton can serve tens or hundreds of models through a model control API. Models can be loaded and unloaded into and out of the inference server based on changes to fit in GPU or CPU memory. Supporting a heterogeneous cluster with both GPUs and CPUs helps standardize inference across platforms and dynamically scales out to any CPU or GPU to handle peak loads.
Key Triton Features
Triton Forest Inference Library (FIL) Backend
The new Forest Inference Library (FIL) backend provides support for high-performance inference of tree-based models with explainability (Shapley values) on CPUs and GPUs. It supports models from XGBoost, LightGBM, scikit-learn RandomForest, RAPIDS cuML RandomForest, and others in Treelite format.
Triton Management Service
Triton Management Service (TMS) addresses the challenge of efficiently scaling Triton instances with a large number of models. TMS is a service that helps when there are more models than can fit in a single GPU and when many Triton instances are needed across servers to handle inference requests from different applications.
Coming Soon
Triton Model Analyzer
Triton Model Analyzer is a tool to automatically evaluate Triton deployment configurations such as batch size, precision, and concurrent execution instances on the target processor. It helps select the optimal configuration to meet application quality-of-service (QoS) constraints—latency, throughput, and memory requirements. It reduces the time needed to find the optimal configuration from weeks to hours.
NVIDIA’s Program for Startups
NVIDIA Inception is a free program designed to help startups evolve faster through access to cutting-edge technology like NVIDIA Triton, NVIDIA experts, venture capitalists, and co-marketing support.
LEARN MORE
You can also download Triton Inference Server on NGC. If you’re interested in enterprise support, learn more about NVIDIA AI Enterprise.