01001
10110
11010
01110
10101
11100
10010
01101
11001
00111
10011
01010
11100
01011
10101
01100

Bringing Generative AI

Within Reach

News
Exciting: DeepFloat11 is here – A Lossless Datatype with 30% Memory Savings, Mathematically Identical Proven backed by Huffman

TRUSTED BY INDUSTRY LEADERS

AWS Activate Logo
NVIDIA Inception Program Logo
XMAD.ai Breakthrough Technology

DF11: a Lossless Datatype with 30% Memory Savings

DeepFloat11

The first mathematically proven method to reduce memory requirements for LLMs without any accuracy loss. Run bigger models on smaller hardware with perfect fidelity.

Memory Optimization

Mathematically proven lossless with no downsides

Standard Format
DF11 Format
70%DF11 Format
100% Mathematically Lossless
Works with Any LLM
Standard Hardware Compatible

Reduce Memory Footprint by 30%

Deploy larger models on existing hardware or reduce infrastructure costs while maintaining perfect accuracy.

Performance Analysis

Why XMAD.ai's DF11 Format Outperforms Other Methods

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.

MethodAccuracy ImpactMemory SavingsImplementation EffortBest For

XMAD.ai DF11

Novel Number Format

Zero Loss (100% Identical)
30%Memory Reduction
Low - Drop-in ReplacementAll Applications

Quantization

8-bit, 4-bit, etc.

Lossy (Accuracy Degradation)
50-75%Memory Reduction
Medium - Requires CalibrationNon-critical Applications

Pruning

Removing Connections

Lossy (Structure Loss)
30-50%Memory Reduction
High - Requires RetrainingSimple Tasks

Distillation

Knowledge Transfer

Lossy (Depth Loss)
50-90%Memory Reduction
Very High - New Model TrainingEdge Devices

The DF11 Advantage

Unlike other methods that trade accuracy for memory savings, DF11 delivers the best of both worlds:significant memory reduction with zero accuracy loss.

100% Lossless

XMAD.ai DF11

Like using a better file format. Same photo quality, smaller file size.

Loses Precision

Quantization

Like using fewer pixels in an image. Smaller, but less detailed.

Loses Connections

Pruning

Like tearing pages from a book. Lighter, but missing content.

Loses Understanding

Distillation

Like turning a textbook into flashcards. Simpler, but less depth.

The Perfect Analogy

DF11 gets you the same image quality with a smaller file size. No compromises, just lossless data representation.

The Future of Model Efficiency

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

30% Memory Savings with No Accuracy Loss

The first mathematically proven method to reduce memory requirements for LLMs without any accuracy loss.

Run Larger Models on Existing Hardware

Deploy more powerful AI models without upgrading your infrastructure.

Immediate Implementation

No retraining required - apply to your existing models today.

Compare the output quality

Select an example prompt

Five monkeys are jumping around on a four poster bed while three chickens stand and watch. How many legs are on the floor?

Logical reasoning

If a clock takes 7 seconds to strike 7 times, how long will the same clock take to strike 10 times?

Logical reasoning

Solve the recurrence relation: T(n) = 2T(n/2) + n log n

Algorithm complexity

Write a function to find the longest increasing subsequence in an array

Dynamic programming

Native(BF16)
Reference
Quality: 100%
Dfloat11XMAD.ai
Our solution
Quality: 100%
4bit
Alternative
Quality: 50%

Who are we?

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.

Fast Fine-Tuning

FineTune OpenSource LLMs

For your agent tasks in hours with no PhDs

Simplified Process

No complex setup required

Rapid Deployment

From data to production in hours

Get Started

Our Latest Insights

Explore our collection of articles on AI compression, model optimization, and industry trends.

Talk to our team

Our support team will get back to you in one business day via email.

Subscribe!

Stay informed and up-to-date with news of our latest features by joining our newsletter.