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What is NVIDIA Triton Inference Server?

The NVIDIA Triton™ inference server delivers powerful and scalable AI solutions tailored for production settings. As an open-source software tool, it streamlines AI inference, enabling teams to deploy trained models from a variety of frameworks including TensorFlow, NVIDIA TensorRT®, PyTorch, ONNX, XGBoost, and Python across diverse infrastructures utilizing GPUs or CPUs, whether in cloud environments, data centers, or edge locations. Triton boosts throughput and optimizes resource usage by allowing concurrent model execution on GPUs while also supporting inference across both x86 and ARM architectures. It is packed with sophisticated features such as dynamic batching, model analysis, ensemble modeling, and the ability to handle audio streaming. Moreover, Triton is built for seamless integration with Kubernetes, which aids in orchestration and scaling, and it offers Prometheus metrics for efficient monitoring, alongside capabilities for live model updates. This software is compatible with all leading public cloud machine learning platforms and managed Kubernetes services, making it a vital resource for standardizing model deployment in production environments. By adopting Triton, developers can achieve enhanced performance in inference while simplifying the entire deployment workflow, ultimately accelerating the path from model development to practical application.

What is LiteRT?

LiteRT, which was formerly called TensorFlow Lite, is a sophisticated runtime created by Google that delivers enhanced performance for artificial intelligence on various devices. This innovative platform allows developers to effortlessly deploy machine learning models across numerous devices and microcontrollers. It supports models from leading frameworks such as TensorFlow, PyTorch, and JAX, converting them into the FlatBuffers format (.tflite) to ensure optimal inference efficiency. Among its key features are low latency, enhanced privacy through local data processing, compact model and binary sizes, and effective power management strategies. Additionally, LiteRT offers SDKs in a variety of programming languages, including Java/Kotlin, Swift, Objective-C, C++, and Python, facilitating easier integration into diverse applications. To boost performance on compatible devices, the runtime employs hardware acceleration through delegates like GPU and iOS Core ML. The anticipated LiteRT Next, currently in its alpha phase, is set to introduce a new suite of APIs aimed at simplifying on-device hardware acceleration, pushing the limits of mobile AI even further. With these forthcoming enhancements, developers can look forward to improved integration and significant performance gains in their applications, thereby revolutionizing how AI is implemented on mobile platforms.

Media

Media

Integrations Supported

PyTorch
TensorFlow
Amazon EKS
Amazon Elastic Container Service (Amazon ECS)
Azure Kubernetes Service (AKS)
Azure Machine Learning
Gemini Enterprise Agent Platform
Google Kubernetes Engine (GKE)
HPE Ezmeral
MXNet
NVIDIA DeepStream SDK
NVIDIA Morpheus
Prometheus

Integrations Supported

PyTorch
TensorFlow
Google AI Edge Gallery
JAX
Java
Kotlin
Objective-C
Python
Swift

API Availability

API Availability

Has API

Pricing Information

Free
Free Version

Pricing Information

Free
Free Version

Supported Platforms

Windows
Mac
Linux

Supported Platforms

Android
iPhone
iPad
Linux

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub
On-Site Training

Training Options

Documentation Hub
Online Training
On-Site Training

Company Facts

Organization Name

NVIDIA

Company Location

United States

Company Website

developer.nvidia.com/nvidia-triton-inference-server

Company Facts

Organization Name

Google

Date Founded

1998

Company Location

United States

Company Website

ai.google.dev/edge/litert

Categories and Features

AI Inference

Not specified

AI Infrastructure

Not specified

Machine Learning

Not specified

ML Model Deployment

Not specified

Categories and Features

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