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NVIDIA Triton Inference Server: Difference between revisions
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{{see also|Model Deployment|artificial intelligence applications}} | |||
== Introduction == | == Introduction == | ||
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NVIDIA Triton Inference Server is an open-source solution that streamlines model deployment and execution, delivering fast and scalable AI in production environments. As a component of the NVIDIA AI platform, Triton allows teams to deploy, run, and scale AI models from any framework on GPU- or CPU-based infrastructures, ensuring high-performance inference across cloud, on-premises, edge, and embedded devices. | NVIDIA Triton Inference Server is an open-source solution that streamlines model deployment and execution, delivering fast and scalable AI in production environments. As a component of the NVIDIA AI platform, Triton allows teams to deploy, run, and scale AI models from any framework on GPU- or CPU-based infrastructures, ensuring high-performance inference across cloud, on-premises, edge, and embedded devices. | ||
== Features == | == Features == | ||
[[File:nvidia triton1.jpg|400px|right]] | |||
=== Support for Diverse Frameworks === | === Support for Diverse Frameworks === | ||
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== Scalability and Integration Ease == | == Scalability and Integration Ease == | ||
[[File:nvidia triton2.jpg|400px|right]] | |||
Available as a Docker container, Triton easily integrates with Kubernetes for orchestration, metrics, and autoscaling. It supports the standard HTTP/gRPC interface for connections with other applications, such as load balancers, and can scale to any number of servers for handling increasing inference loads for any model. | Available as a Docker container, Triton easily integrates with Kubernetes for orchestration, metrics, and autoscaling. It supports the standard HTTP/gRPC interface for connections with other applications, such as load balancers, and can scale to any number of servers for handling increasing inference loads for any model. | ||
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NVIDIA continues to invest in Triton's development, incorporating new features and improvements based on user feedback and industry needs. Upcoming advancements may include additional framework support, improved orchestration capabilities, enhanced performance optimization, and more. | NVIDIA continues to invest in Triton's development, incorporating new features and improvements based on user feedback and industry needs. Upcoming advancements may include additional framework support, improved orchestration capabilities, enhanced performance optimization, and more. | ||
[[Category:Model Deployment]] [[Category:Inference]] [[Category:Servers]] [[Category:DevOps]] | |||