Triton Inference Server

Triton Inference Server is an open-source software platform designed by NVIDIA to streamline and optimize the deployment and execution of AI models across various environments and hardware configurations. It supports a wide range of deep learning and machine learning frameworks, including TensorRT, TensorFlow, PyTorch, ONNX, OpenVINO, Python, RAPIDS FIL, and others, making it highly versatile for different AI applications[1].

Triton Inference Server is designed to work across different infrastructures, including cloud, data center, edge, and embedded devices, and it supports NVIDIA GPUs, x86, ARM CPUs, and AWS Inferentia for inference tasks. This flexibility allows teams to deploy AI models in the most efficient manner possible, tailored to their specific needs[1].

One of the key features of Triton Inference Server is its ability to deliver optimized performance for a variety of query types, such as real-time, batched, ensembles, and audio/video streaming. It includes support for stateful models, dynamic batching, and model versioning, among other features. Triton also allows for the addition of custom backends and pre/post-processing operations, enabling further customization and optimization of AI model deployments[1].

Triton Inference Server is part of NVIDIA AI Enterprise, which provides enterprise-grade support, security, stability, and manageability for AI deployments. This includes access to NVIDIA’s global support and additional enterprise services, ensuring that organizations can deploy and manage their AI applications with confidence[3].

For developers and organizations looking to deploy AI models at scale, Triton Inference Server offers a comprehensive solution that simplifies the process while maximizing performance and efficiency. Its support for multiple frameworks and hardware configurations, along with its extensive feature set, makes it a powerful tool for deploying AI models in any environment[1][2][3].

Citations:

[1] https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/index.html

[2] https://developer.nvidia.com/triton-inference-server

[3] https://www.nvidia.com/en-us/ai-data-science/products/triton-inference-server/get-started/

[4] https://www.nvidia.com/en-us/ai-data-science/products/triton-inference-server/

[5] https://developer.nvidia.com/blog/power-your-ai-inference-with-new-nvidia-triton-and-nvidia-tensorrt-features/

[6] https://www.coreweave.com/blog/serving-inference-for-llms-nvidia-triton-inference-server-eleuther-ai

[7] https://github.com/triton-inference-server/server

[8] https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/user_guide/optimization.html

[9] https://docs.nvidia.com/launchpad/ai/classification-openshift/latest/openshift-classification-triton-overview.html

[10] https://github.com/triton-inference-server

[11] https://esf.eurotech.com/docs/nvidiatm-triton-server-inference-engine

[12] https://cloud.google.com/vertex-ai/docs/predictions/using-nvidia-triton

[13] https://www.run.ai/guides/machine-learning-engineering/triton-inference-server

[14] https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/user_guide/faq.html