NVIDIA Triton Inference Server: Difference between revisions

no edit summary
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 ==
{{#ev:youtube|1kOaYiNVgFs|400|right}}
[[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.


370

edits