Product Descriptions
Speed of light, precision foundation model
Compress AI models to enjoy the benefits of efficient and portable models.
Significantly reduce memory and disk space requirements, making AI projects much more affordable.
CompactifAI 사용의 장점
Cost Reduction
Reduce energy costs and hardware expenses.
Privacy Protection
Secure your data with localized AI models that do not rely on cloud-based systems.
Speed Enhancement
Overcome hardware limitations and accelerate AI-based projects.
Sustainability
Reduce energy consumption and contribute to a greener planet.
Why CompactifAI?
Modern AI models are facing serious inefficiencies, with the number of parameters growing exponentially while accuracy improves only linearly. This imbalance leads to the following issues
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Exponential Increase in Computing Power Usage
The required computational resources are increasing at an unsustainable rate.
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Exponential Rise in Energy Costs
Increased energy consumption not only impacts costs but also raises environmental concerns.
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Limited Supply of High-End Chips
The shortage of advanced chips restricts innovation and business growth.
CompactifAI Solutions
Revolutionizing 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 the development and deployment of smaller, specialized AI models locally, ensuring efficient and task-specific solutions.
Privacy Protection & Governance Compliance
Supports the ethical, legal, and secure use of AI technology by fostering a private and safe environment.
Portability
Compress models to enable deployment on any device.
CompactifAI Key Features
Size Reduction
Reduction in Parameter Count
Faster Inference
Faster Retraining
Latest Benchmarks vs. Llama 2-7B
CompactifAI revolutionizes AI model efficiency and portability, providing numerous benefits such as cost savings, privacy protection, speed improvements, and sustainability. This allows AI projects to be executed more affordably and effectively.
| Metric | Value |
|---|---|
| Model Size Reduction | +93% |
| Parameter Reduction | +70% |
| Accuracy Loss | 2%-3% 미만 |
| Inference Time Reduction | 88% -> 24%-26% | 93% -> 24%-26% |
| Methodology: Tensorization + Quantization | |