CompactifAIMultiverse Computing
|
Intel Open Edge PlatformIntel
|
|||||
Related Products
|
||||||
About
CompactifAI from Multiverse Computing is an AI model compression platform designed to make advanced AI systems like large language models (LLMs) faster, cheaper, more energy efficient, and portable by drastically reducing model size without significantly sacrificing performance. Using advanced quantum-inspired techniques such as tensor networks to “compress” foundational AI models, CompactifAI cuts memory and storage requirements so models can run with lower computational overhead and be deployed anywhere, from cloud and on-premises to edge and mobile devices, via a managed API or private deployment. It accelerates inference, lowers energy and hardware costs, supports privacy-preserving local execution, and enables specialized, efficient AI models tailored to specific tasks, helping teams overcome hardware limits and sustainability challenges associated with traditional AI deployments.
|
About
The Intel Open Edge Platform simplifies the development, deployment, and scaling of AI and edge computing solutions on standard hardware with cloud-like efficiency. It provides a curated set of components and workflows that accelerate AI model creation, optimization, and application development. From vision models to generative AI and large language models (LLM), the platform offers tools to streamline model training and inference. By integrating Intel’s OpenVINO toolkit, it ensures enhanced performance on Intel CPUs, GPUs, and VPUs, allowing organizations to bring AI applications to the edge with ease.
|
|||||
Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
|
Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
|
|||||
Audience
AI developers, machine learning engineers, and organizations that need to deploy large language models (LLMs) and other AI systems more efficiently, cost-effectively, and sustainably
|
Audience
Businesses and developers looking for a powerful, scalable solution to build and deploy AI applications at the edge, leveraging Intel’s optimized hardware and cloud-like simplicity for edge computing
|
|||||
Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Supported
|
Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Supported
|
|||||
API
Offers API
Supported
|
API
Offers API
Not Supported
|
|||||
Screenshots and Videos |
Screenshots and Videos |
|||||
Pricing
No information available.
Free Version
Not Supported
Free Trial
Not Supported
|
Pricing
No information available.
Free Version
Not Supported
Free Trial
Not Supported
|
|||||
Reviews/
|
Reviews/
|
|||||
Training
Documentation
Supported
Webinars
Not Supported
Live Online
Supported
In Person
Not Supported
|
Training
Documentation
Supported
Webinars
Not Supported
Live Online
Not Supported
In Person
Not Supported
|
|||||
Company InformationMultiverse Computing
Founded: 2019
Basque Country
multiversecomputing.com/compactifai
|
Company InformationIntel
Founded: 1968
United States
www.intel.com/content/www/us/en/developer/tools/tiber/edge-platform/overview.html
|
|||||
Alternatives |
Alternatives |
|||||
|
|
|
|||||
|
|
|
|||||
Categories |
Categories |
|||||
Integrations
Amazon Web Services (AWS)
Supported
Cosmian
Not Supported
Depot
Not Supported
Dive
Not Supported
Google Cloud Confidential VMs
Not Supported
Hugging Face
Not Supported
Intel Geti
Not Supported
Intel SceneScape
Not Supported
Intel Tiber AI Cloud
Not Supported
JupyterLab
Not Supported
|
Integrations
Amazon Web Services (AWS)
Not Supported
Cosmian
Supported
Depot
Supported
Dive
Supported
Google Cloud Confidential VMs
Supported
Hugging Face
Supported
Intel Geti
Supported
Intel SceneScape
Supported
Intel Tiber AI Cloud
Supported
JupyterLab
Supported
|
|||||
|
|
|