CompactifAI

The AI model compressor to make AI systems faster, cheaper and energy efficient.

Have your AI model compressed and benefit from efficient and portable models. Greatly reducing the requirements for memory and diskspace, making AI projects much more affordable to implement.

Benefits of Using CompactifAI
Cost Savings icon

Cost Savings

Lower your energy bills and reduce hardware expenses.

Privacy icon

Privacy

Keep your data safe with localized AI models that don't rely on cloud-based systems.

Speed icon

Speed

Overcome hardware limitations and accelerate your AI-driven projects.

Sustainability icon

Sustainability

Contribute to a greener planet by cutting down on energy consumption.

Why CompactifAI?

Current AI models face significant inefficiencies, with parameter counts growing exponentially but accuracy only improving linearly.

This imbalance leads to:

Skyrocketing Computing Power Demands

Skyrocketing Computing Power Demands

The computational resources required are growing at an unsustainable rate.

Soaring Energy Costs

Soaring Energy Costs

Increased energy consumption not only impacts the bottom line but also raises environmental concerns.

Limited Chip Supply

Limited Chip Supply

The scarcity of advanced chips limits innovation and business growth.

The Solution

Revolutionizing AI Efficiency and Portability: CompactifAI leverages advanced tensor networks to compress foundational AI models, including large language models (LLMs).

This innovative approach offers several key benefits:

Enhanced efficiency

Drastically reduces the computational power required for AI operations.

Specialized AI models

Enables the development and deployment of smaller, specialized AI models locally, ensuring efficient and task-specific solutions.

Privacy and Governance Requirements

Supports the development of private and secure environments, crucial to ensure ethical, legal, and safe use of AI technologies.

Portability

Compress the model and put it on any device.

Key Features

Size Reduction

Parameter Reduction

Faster Inference

Faster Retraining

Recent benchmarks with Llama 2-7B

MetricValue
Model Size Reduction+93%
Parameter Reduction+70%
Accuracy LossLess than 2%-3%
Inference Time Speed Up
88% -> 24%-26%93% -> 24%-26%
By tensorization + quantization
Learn more

Watch the video

Abstract Brain Representation

Read the paper

FAQ

Ready to transform your AI Capabilities?

Contact us today to learn how CompactifAI can streamline your AI operations and drive your business forward.