Unlock the Potential of AI
Deploy your AI with maximal efficiency
Consider AI compression if you're experiencing any of the following issues
Challenges
Sky-high AI Operation Costs
Excessive GPU cloud costs for operating the AI models.
Latency in the Key User Flow
The AI model in the service is too slow, negatively affecting the user experience.
Need for On-Device AI
In certain situations, deploying AI on devices is necessary to boost security, lower costs, and enhance user experience.
Understanding AI Model Compression
AI model compression reduces the size of neural networks, requiring fewer computations and improving computational efficiency.
AI compression involves a tradeoff between speed and performance. Multiple tests are needed to determine the conditions where the tradeoff is minimal and to identify the best tradeoffs for your specific use case.
Key Benefits of AI Model Compression
Faster
From research to real-world application
Cost Effective
Achieve the same performance at a lower cost
On-Device AI
Smaller size, broader applications
How to Compress an AI Model Efficiently
Quantization is a method to make AI models more lightweight by reducing the precision of data representation. Data typically represented as 32-bit or 16-bit floating points can be compressed to 8 bits or even as low as 4, 2, or 1 bit.
AI Compression Toolkit
OwLite is the most accessible AI compression toolkit, simplifying applying advanced AI compression techniques to pre-existing machine learning models.
Compress Your AI Model with Us
SqueezeBits excels in optimizing deep learning models using state-of-the-art model compression techniques.
