Edge Model Foundry
Your next edge product does not need a bigger processor.
It needs several small ones, each carrying a different skill. That is how speech, vision and condition monitoring get into a device whose compute costs four dollars a part, and how you find out it fits before you commit to hardware.
on real silicon
three parts
volume
the device
yours to keep
Why teams choose this
What changes when intelligence is something you add, not something you specify.
Find out it fits while the design is still cheap to change
The expensive moment in an edge programme is discovering, after the board is laid out, that the model does not fit the part. Send us the model and get back the flash it occupies, the working memory it needs and the milliseconds it takes, measured on the silicon you are considering.
That answer currently costs an engineer a week and a development board. It should cost an afternoon.
Add a skill by adding a part
Halfway through a programme the requirement changes: it needs to hear as well as see. On a single processor that is a re-spin, a new thermal budget and a schedule slip.
Here it is another inexpensive part carrying another model, wired into the same graph. Capability scales by addition, and the parts you are not using draw almost nothing.
Nothing leaves the device, so the conversation is shorter
Plants that will not put a microphone on a network. Defence programmes that will not accept a call home. Products where a recording reaching a cloud is the objection that ends the sale.
Inference happens on the part. There is no gateway, no network dependency and nothing to exfiltrate, which turns a procurement blocker into a line in the datasheet.
Instrument forty points for what four used to cost
Condition monitoring is well understood and priced as a capital project, because the sensing hardware is expensive. Change the cost of a sensing point and the arithmetic changes with it.
Covering a whole line stops being a proposal that needs board approval and becomes an operating line item, which is usually the difference between a pilot and a rollout.
The idea
More parts, not bigger ones.
Splitting work across cheap parts to finish sooner is ordinary parallelism, and a larger processor would do the same. That is not the argument.
The argument is that capability composes. Each part holds one skill. Together they do what none of them could hold, and no single processor in this class runs the whole system at any clock speed.
This is an old idea that was priced out of reach. Minsky argued that intelligence is what a large number of small, individually unintelligent processes produce between them, and Hillis built the machine that took the idea seriously. It cost what a research instrument costs, because the processors were expensive. They are not any more.
A camera frame split into twelve overlapping windows across four parts, each answer returned to its origin.
Two presence models pooled, against 0.769 for the stronger one alone. The cost was one more part.
Writing a model to five parts runs at 444 KB/s against 108 KB/s to one. Fleets update in parallel.
A complete prompt-to-image generator, with no dynamic allocation anywhere in the path.
How you get there
Take the models, the runtime, or the finished machine.
Most teams start at the free runtime and move up only when it earns its place.
Start with models that already fit
Reading, listening, watching and monitoring, published with the footprint and the latency they carry on real silicon. Take them as they are, or have one trained for your problem.
Describe the outcome, not the wiring
Say what should happen to the data. The runtime works out which parts get which models, writes them, distributes the work and reports what each one did. It is Apache 2.0 and it is yours.
Compose a system that outgrows one part
Several models behaving as one product. Parts stay dark until work reaches them, so the power a machine draws follows what it is doing rather than everything it might be asked.
Try it on five parts, free, for as long as you like.
The free tier is five boards rather than one, because a single part cannot demonstrate the thing this is for. The whole argument is what happens when several narrow models are wired together, and a trial that cannot show that is not a trial.
The runtime stays Apache 2.0 whatever you decide afterwards. Prove a machine here and run the identical graph air-gapped on your own hardware.
The whole runtime, unlimited boards, commercial use, no expiry and no call home.
A hosted place to build machines, with fit data measured against your parts.