LLM
2 bits
Diffora
1,000 bits
Human
1,600 bits

Diffora Next Generation Foundation Models

A fundamentally new architecture that stores 10× more knowledge per parameter than any LLM.

10×
Parameter Efficiency
20
Bits per Parameter
1000
Bit Scaling Target

AI research is stuck.
Models are hitting a wall.

No architecture has superseded the LLM in years — but there are worlds beyond it.

This is Diffora.
1.5 years of research Breakthrough · March 2026 Just getting started

A 10× Leap in Parameter Efficiency

LLMs store ~2 bits of knowledge per parameter. Diffora reaches ~20 — built to scale toward the ~1,600 bits of a biological synapse.

Bits per Parameter

Knowledge storage capacity
Current LLMs~2
Diffora (Current)~20
Diffora (Target)~1,000
Biological Neurons~1,600

A Small Model That Learns Like a Giant

Validation loss during training — a 2.32M-parameter Diffora follows the learning curve of an LLM nearly 10× its size.
Diffora 2.32M LLM 3.67M LLM 21.40M
1.50 1.60 1.70 1.80 1.90 500 1500 2500 3500 4500 Training Step Validation Loss Diffora · 2.32M LLM · 3.67M LLM · 21.40M

Efficient Intelligence

🧠

Intelligence per Parameter

More knowledge in every parameter than any similarly sized LLM.

🪶

10× Smaller, Same Power

Matches LLMs ten times its size — with 100–1000× ahead.

🌀

The RL Unlock

Reinforcement learning unlocks capabilities no LLM can reach.

Beyond Nvidia

Bypassing Nvidia makes trillion-parameter models radically cheap.

From 10× to 1000×

10×
Today
100–1000×
With Reinforcement Learning
$5
Per 100M Tokens
01

Text-to-Speech

Studio-quality voice, anywhere.

02

On-Device Models

Frontier AI in your pocket.

03

Robotics

Real-time precision control.

04

Coding Models

Beyond anything available.

Get in Touch

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