The first mathematically proven method to reduce memory requirements for LLMs without any accuracy loss. Run bigger models on smaller hardware with perfect fidelity.
Mathematically proven lossless with no downsides
Deploy larger models on existing hardware or reduce infrastructure costs while maintaining perfect accuracy.
Most approaches to reducing AI model size sacrifice accuracy. XMAD.ai's DF11 format is different - it's the first mathematically proven method that delivers 30% memory savings with absolutely no loss in model performance.
| Method | Accuracy Impact | Memory Savings | Implementation Effort | Best For |
|---|---|---|---|---|
XMAD.ai DF11 Novel Number Format | Zero Loss (100% Identical) | 30%Memory Reduction | Low - Drop-in Replacement | All Applications |
Quantization 8-bit, 4-bit, etc. | Lossy (Accuracy Degradation) | 50-75%Memory Reduction | Medium - Requires Calibration | Non-critical Applications |
Pruning Removing Connections | Lossy (Structure Loss) | 30-50%Memory Reduction | High - Requires Retraining | Simple Tasks |
Distillation Knowledge Transfer | Lossy (Depth Loss) | 50-90%Memory Reduction | Very High - New Model Training | Edge Devices |
Unlike other methods that trade accuracy for memory savings, DF11 delivers the best of both worlds:significant memory reduction with zero accuracy loss.
Like using a better file format. Same photo quality, smaller file size.
Like using fewer pixels in an image. Smaller, but less detailed.
Like tearing pages from a book. Lighter, but missing content.
Like turning a textbook into flashcards. Simpler, but less depth.
DF11 gets you the same image quality with a smaller file size. No compromises, just lossless data representation.
We believe DF11 marks the beginning of a new era in model memory efficiency—where developers no longer have to choose between size and quality. This isn't a compromise. It's a breakthrough, and we're excited to help the AI community and see the adoption of DF11, feel free to try it to try it out by yourself
The first mathematically proven method to reduce memory requirements for LLMs without any accuracy loss.
Deploy more powerful AI models without upgrading your infrastructure.
No retraining required - apply to your existing models today.
Logical reasoning
Logical reasoning
Algorithm complexity
Dynamic programming
We're a frontier AI agent research lab firmly focused on advancing agent technology beyond current limitations. We believe that the current approaches most companies are taking with prompt engineering and RAG are ultimately a dead end for creating truly effective AI agents.
Our team brings together top researchers in model reinforcement learning, fine-tuning, synthetic data generation, performance optimization, and distributed systems. We're uniquely positioned at the intersection of cutting-edge research and practical enterprise applications.
For your agent tasks in hours with no PhDs
No complex setup required
From data to production in hours
Explore our collection of articles on AI compression, model optimization, and industry trends.
Apr 18 2025
Our support team will get back to you in one business day via email.
