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The Compression Layer for AI
30x smaller models. Same intelligence.
80% of inference cost is moving weight bytes from memory — not computing.
Now multiply by every domain specialist an enterprise needs: medical, legal, finance, support, compliance...
100 specialized 7B models = 1.4 TB of weights.
On a single GPU? Impossible.
The industry spends $50B+/year on inference infrastructure. Most of it is moving bytes, not doing math.
The gap: No existing method achieves >4x compression while preserving downstream task quality on reasoning benchmarks.
We close that gap. By 10-20x.
Proprietary training method producing models with 8-57x fewer stored parameters while maintaining full performance on standard benchmarks.
Composes with quantization (our compression x INT4 = multiplicative)
No decompression at inference — weights never fully materialized
Compute overhead: ~6% (negligible vs memory savings)
Both models trained on identical data (5B tokens, OpenWebText). Fair comparison.
The compact 7B stores 88% fewer parameters than the 1.5B standard yet beats it on all six benchmarks.
At 250B: Standard needs 7 interconnected chips ($200K). Compact fits on one chip.
Results at 1.5B scale. These are expected baselines for models trained on 5B tokens — the point is parity at 8-57x compression, not absolute score.
Bold = matches or beats standard.
At 8x: matches on every benchmark, beats on MMLU (+1.4pp) and ARC-C (+0.8pp)
At 25x: ties Standard on ARC-Challenge — the hardest reasoning test
At 57x: matches Standard on MMLU despite storing 57x fewer parameters
Validated across 4 architectures and 3 modalities.
This is a universal property of multi-layer transformers.
Same quality. 1,170x less storage. 12 ms hot-swap. Per-request domain routing from one GPU.
Converting existing models to compact form fails catastrophically (1,000x worse). Models must be trained with our method from the beginning.
Prior work achieves 2-3x. We achieve 8-57x. The gap is 10-20x. Peer-reviewed publication forthcoming.
Training a 70B compact model costs $500K+ in compute. Our pre-trained model zoo is the moat.
Cortex SDK + model zoo + specialist marketplace. Once customers build on our format, switching means retraining everything.
We have working 7B models today. Replication from a paper takes 6-12 months minimum.
Multi-tenant inference. 100 specialists on 1 GPU.
$0.10/M tokens (10x cheaper at same quality). 80%+ gross margin.
Bring your architecture, we train it compact.
$50K-500K per engagement. 10-20% of original training cost.
Upload domain data, get a 5 MB specialist module.
Self-service. $50-5K per specialist.
Future: License compact-optimized ASIC pipeline design to chip makers. Royalty per chip. Cycle-accurate simulation validated, 0% overhead.
GPU cost is #1 expense. 30x fewer GPUs per model.
Need 10-100 specialists. 1 base + 5 MB each.
Models too large for phones/IoT. 7B in 200 MB.
Memory bandwidth is the wall. 30x less data to move.
Can't send data to cloud. Full model on local hardware.
Beachhead: Enterprise multi-tenant serving in regulated verticals (healthcare, finance, legal) where domain specialists are needed and data can't leave the building.
10-20x ahead of nearest competitor. Our compression stacks with quantization: Compact + INT4 = 120x total reduction.
PhD in Infobionics (Nonlinear Networks & Chaos Theory) — Budapest, 2008, Summa Cum Laude
Research collaboration with Leon Chua, UC Berkeley (memristor dynamics, IEEE Cover)
Leaders in Innovation Fellow, Royal Academy of Engineering
2 US Patents in computing architectures
Built mLabs developer community (20,000+ members)
20 years applying nonlinear mathematics to real-world systems
Hiring with this round: 2-3 ML engineers (systems + training), 1 product engineer
Seed Round · 2026
Models getting bigger every quarter. Compression value grows with scale.
Inference cost now exceeds training cost industry-wide.
On-device AI exploding. Models are too big. We make them fit.
Multi-tenant demand has no efficient solution today.
20 years of nonlinear mathematics research led to this — not accidental.
Working models, not theory. Benchmarked across 4 architectures.
10-20x ahead of nearest published work.
Replication takes 6-12 months even with the paper.
Nature runs the human brain — 100 trillion synaptic connections — on 20 watts. Not by making neurons bigger. By making structure recursive.
We apply the same principle to artificial intelligence.
nonlinear.technology
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