Discovery
How small it can go, and what that does to accuracy.
Compress
Compress a model
Compress your model with patent-pending PNN.
You already trained the model. Others want you to throw it away and train a smaller stand-in. We compress that checkpoint — same architecture, fewer parameters, scored on the eval you name.
Distil Labs, OpenDistil, Vertex, Bedrock train a smaller different student for a task. We shrink the model you already have.
What you buy
- ·You already trained the model. We compress that checkpoint — same architecture, fewer parameters, scored on the eval you name.
- ·Three packages by outcome: Discovery, Advanced, Expert.
Packages
Advanced
Your compressed model, plus that measurement.
Expert
A size or model type we don’t already run.
Evidence
ViT-B/16, ImageNet-1k, 34% uniform (one seed):
77.58% vs dense 84.51%
One seed. No latency. No energy. Other architectures and densities: measured on your eval after Discovery.
Request a quote
No cart. No account. Submit — we send a plan and a quote.