370
edits
No edit summary |
No edit summary |
||
Line 1: | Line 1: | ||
== Introduction == | == Introduction == | ||
{{#ev:youtube|1kOaYiNVgFs|400|right}} | |||
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 === | ||
Line 25: | Line 26: | ||
== 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. | ||
edits