Product Descriptions
The speed of light, a fine foundation model
Compress AI models to enjoy the benefits of efficient and portable models. Significantly reduce memory and disk space requirements to implement AI projects much more affordably.
Benefits of Using CompactifAI
Cost Reduction
Reduce energy costs and lower hardware expenses.
Privacy Protection
Safely protect data with localized AI models that don't rely on cloud-based systems.
Speed Improvement
Overcome hardware limitations and accelerate AI-based projects.
Sustainability
Overcome hardware limitations and accelerate AI-based projects.
Why CompactifAI?
Current AI models face serious inefficiencies, with the number of parameters increasing exponentially while accuracy only improves linearly. This imbalance causes the following problems:
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Exponential Increase in Computing Power Usage
Required computational resources are increasing at an unsustainable rate.
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Exponential Increase in Energy Costs
Increased energy consumption not only affects costs but also causes environmental issues.
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Limited Supply of High-Spec Chips
The shortage of advanced chips limits innovation and business growth.
CompactifAI's Solution
Innovation in AI Efficiency and Portability: CompactifAI
CompactifAI leverages advanced tensor networks to compress foundational AI models, including large language models (LLMs). This innovative approach provides several key benefits:
Enhanced Efficiency
Dramatically reduces the computational power required for AI operations.
Specialized AI Models
Enables 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 to ensure ethical, legal, and safe use of AI technology.
Portability
Compress models to fit on any device.
CompactifAI Key Features
Size Reduction
Reduction in Number of Parameters
Faster Inference
Faster Retraining
Latest Benchmark with Llama 2-7B
CompactIfAI innovatively improves the efficiency and portability of AI models, enabling cost reduction and privacy protection, allowing AI projects to be implemented more affordably and effectively.
| Metric | Value |
|---|---|
| Model Size Reduction | +93% |
| Parameter Reduction | +70% |
| Accuracy Loss | Less than 2%-3% |
| Inference Time Reduction | 88% -> 24%-26% | 93% -> 24%-26% |
| Method: Tensorization + Quantization | |