OEM & Licensing

Bigger models shouldn't need bigger hardware.

Simulation validated at circuit level · Seeking hardware partners

AI memory is sold out. HBM prices surged 246% in 2025. DRAM is up 50-55% this quarter alone. NVIDIA is cutting GPU production 30-40% because there isn't enough memory to build them. Every factory is at capacity. The shortage extends through 2026 and beyond.

The industry's answer is to build more memory factories. Ours is to need dramatically less memory.

25x
Less memory per model
750 MB
7B-class model footprint
200 MB
With quantization

The memory crisis in numbers

HBM price increase (2025)+246%
DRAM price increase (Q1 2026 vs Q4 2025)+50-55%
GPU production cuts (NVIDIA RTX 50-series)30-40%
Memory demand growth (2026)+35%
Memory supply growth (2026)+23%
HBM sold out through2026+

The gap between demand (+35%) and supply (+23%) is widening. Building new fabs takes 3-4 years. The memory crisis is structural, not cyclical.

Our architecture is the fastest way to close that gap. Not by making more memory - by making AI need dramatically less of it.

What we license

A proprietary model architecture that compresses AI models by 25-60x while preserving quality on standard benchmarks. The compression is structural, not a post-processing step. The architecture is designed from the ground up to be compact.

Order-of-magnitude smaller models
Composes with quantization
Hundreds of specialists, one GPU
Millisecond domain switching
Multiplicative with standard compression
Negligible compute overhead

Validated across five modalities

Language (7B-class)Matches standard
Vision (classification)-0.7% accuracy at 5x
Diffusion (image generation)+3.3 FID at 5x
DNA GenomicsBeats 47x larger models
Protein modelling153x smaller than ESM-2

Who this is for

GPU / accelerator companies. Your customers can't get enough memory. Our architecture lets them run larger models on your existing hardware. License our compression layer - your chips serve 25x more customers.

Cloud providers. Multi-tenant AI serving is memory-bound. 100 domain specialists need 1,400 GB on standard architecture. On ours: 1.3 GB. Same GPU serves 1000x more tenants.

Device manufacturers. Smartphones, wearables, edge appliances. A 7B model that fits in 200 MB (INT4) runs on hardware designed for 1B models. Your next product ships with 7B intelligence.

Analog / in-memory compute startups. Your chip's crossbar arrays are too few for standard transformers. Our architecture significantly reduces array requirements. Circuit-level simulation available for evaluation.

How we work

IP licensing. We license the architecture. You integrate into your platform, SDK, or silicon. Recurring royalty. We provide reference implementations, pre-trained models, and integration support.

Joint development. We co-design compressed models optimized for your specific hardware. Your silicon expertise, our compression architecture. Shared IP on the joint work.

Evaluation. We provide a technical evaluation package - pre-trained models, benchmark results, integration guide. You validate on your hardware before committing. No risk.

The memory crisis has an architectural solution.

We're looking for hardware companies, cloud providers, and device manufacturers who want to ship AI products without waiting for the memory supply chain to catch up.

Discuss licensing →