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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.

What is Google Cloud Deep Learning VM Image?

Rapidly establish a virtual machine on Google Cloud for your deep learning initiatives by utilizing the Deep Learning VM Image, which streamlines the deployment of a VM pre-loaded with crucial AI frameworks on Google Compute Engine. This option enables you to create Compute Engine instances that include widely-used libraries like TensorFlow, PyTorch, and scikit-learn, so you don't have to worry about software compatibility issues. Moreover, it allows you to easily add Cloud GPU and Cloud TPU capabilities to your setup. The Deep Learning VM Image is tailored to accommodate both state-of-the-art and popular machine learning frameworks, granting you access to the latest tools. To boost the efficiency of model training and deployment, these images come optimized with the most recent NVIDIA® CUDA-X AI libraries and drivers, along with the Intel® Math Kernel Library. By leveraging this service, you can quickly get started with all the necessary frameworks, libraries, and drivers already installed and verified for compatibility. Additionally, the Deep Learning VM Image enhances your experience with integrated support for JupyterLab, promoting a streamlined workflow for data science activities. With these advantageous features, it stands out as an excellent option for novices and seasoned experts alike in the realm of machine learning, ensuring that everyone can make the most of their projects. Furthermore, the ease of use and extensive support make it a go-to solution for anyone looking to dive into AI development.

Media

Media

Integrations Supported

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

Integrations Supported

PyTorch
TensorFlow
Chainer
Google Cloud Platform
Google Cloud TPU
Google Compute Engine
JupyterLab
MXNet
NVIDIA DRIVE

API Availability

Has API

API Availability

Pricing Information

Free
Free Version

Pricing Information

Pricing not provided
Free Trial Offered?

Supported Platforms

Android
iPhone
iPad
Linux

Supported Platforms

SaaS

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars

Company Facts

Organization Name

Google

Date Founded

1998

Company Location

United States

Company Website

ai.google.dev/edge/litert

Company Facts

Organization Name

Google

Date Founded

1998

Company Location

United States

Company Website

cloud.google.com/deep-learning-vm

Categories and Features

Categories and Features

AI Infrastructure

Not specified

Deep Learning

Not specified

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