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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 CompactifAI?

CompactifAI, a groundbreaking platform created by Multiverse Computing, focuses on compressing AI models to improve the speed, cost-effectiveness, energy efficiency, and portability of sophisticated AI systems, including extensive language models, by substantially reducing their size while ensuring consistent performance. Utilizing state-of-the-art quantum-inspired techniques like tensor networks for the compression of core AI models, CompactifAI adeptly lowers memory and storage requirements, enabling these models to run with reduced computational power and be implemented across diverse environments, such as cloud, on-premises, edge, and mobile applications, via a managed API or private deployment. This platform not only boosts inference speed and curtails energy and hardware costs but also promotes privacy-focused local execution and aids in the development of tailored, efficient AI models that are fine-tuned for specific tasks. Ultimately, this innovation assists teams in overcoming the hardware constraints and sustainability challenges frequently faced in conventional AI applications. Moreover, by providing greater flexibility in deployment, CompactifAI allows organizations to harness advanced AI capabilities in a wider array of scenarios than previously possible, paving the way for novel applications and solutions in various fields.

Media

Media

Integrations Supported

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

Integrations Supported

Amazon Web Services (AWS)
Llama
Mistral AI

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Version

Pricing Information

Pricing not provided

Supported Platforms

Android
iPhone
iPad
Linux

Supported Platforms

SaaS
On-Prem

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Online Training
On-Site Training

Training Options

Documentation Hub
Online Training

Company Facts

Organization Name

Google

Date Founded

1998

Company Location

United States

Company Website

ai.google.dev/edge/litert

Company Facts

Organization Name

Multiverse Computing

Date Founded

2019

Company Location

Basque Country

Company Website

multiversecomputing.com/compactifai

Categories and Features

Categories and Features

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