Only five companies can afford to build frontier AI. We think that's a bug.
Intelligence is gated by capital, not ideas.
Training a frontier model costs $100 million to $1 billion. It demands purpose-built data centers — thousands of specialized chips, custom networking, and megawatts of continuous power.
The open-source community has the talent and the will, but not the hardware. Universities, startups, developing nations, and independent researchers are locked out of the most important technology of the century. The barrier was never ideas. It was money.
Everyone is optimizing the same trade. Fusion Training abandons it.
It doesn't fit the existing buckets.
Every other method either rebuilds the model from scratch or leaves its capacity untouched. Fusion Training grows a trained model — adding real new neurons and connections — while preserving everything it already knows.
Diagnose. Expand. Train. Optimize. Repeat.
Think of a model like a brain. Today, a smarter brain has to be grown from scratch in an expensive factory. Fusion Training grows the brain you already have — surgically, and on a desktop.
Scan the model to see which parts are working hard and which have spare capacity. The result is a map of exactly where it needs more intelligence.
Surgically add new neurons where capacity is needed. A function-preserving technique guarantees the model behaves exactly as before — nothing lost, nothing broken. It's just bigger now, with room to learn.
Train only the new neurons. The original model stays frozen. Because just a few layers learn at a time, you need a fraction of the compute traditional training demands.
Scan the larger model for dead weight — neurons left over from the original training that aren't pulling their weight — and remove them. The model gets smaller and faster with zero quality loss. We call it inverse fusion.
Each cycle makes the model smarter. Run it nightly, weekly, or continuously. The model evolves over time instead of being rebuilt from scratch every 6–12 months.
A $4,000 desktop matched a $25,000 GPU. To within 0.04%.
Validated across 26 sessions.
Every experiment ran on an NVIDIA DGX Spark — a desktop computer that draws 65 watts and sits on a desk — compared against an NVIDIA A100, the $25,000 professional GPU that is the industry standard. Same starting model, same data, same number of steps. The only difference was the method.
Lower perplexity is better. Fusion Training didn't just match quality — given equivalent compute, it built a larger, more capable model (1.55B → 2.6B parameters) that beat the A100 outright, because the expanded architecture had more room to learn.
Growth cycles compound
We ran 6 consecutive growth cycles. Quality improved every single time. The model gets smarter with every iteration.
Contraction works
After growing the model, we identified and removed dead weight — reducing size by 10% with zero quality loss. Smarter and more efficient.
Architecture-agnostic
Validated on a non-standard hybrid architecture combining Transformer and Mamba layers. It worked on both. Standard models like Llama, Qwen, and Mistral are simpler.
Scales without limit
Fusion trains a few layers at a time, using ~24 GB of memory regardless of total model size. A 1B model and a 1T model need the same memory — only the time changes.
The industry's core assumption was wrong.
Capital
Frontier training currently costs $100M–$5B. Fusion reduces it to the cost of a desktop and electricity. Any organization can grow AI models.
Energy
Comparable results at 1/600th to 1/4000th the energy. A 65-watt desktop replaces a rack of GPUs drawing kilowatts — the compliant path as regulators scrutinize AI's power appetite.
Continuous improvement
Today's models are frozen snapshots, replaced every 6–12 months at enormous cost. Fusion models improve nightly. No more launches — the model simply gets better every day.
Distributed training
No GPU-to-GPU communication required. A thousand desktops on ordinary internet can collectively train a model no single data center could afford to build from scratch.