Sfiniti runs open-weight models on the computers you already own, coordinates concurrent work through a shared local model, and reaches for the frontier with your provider keys, connecting your machine straight to your provider. We are software, never a middleman. Your prompts and keys stay on your hardware.
Sfiniti is a runtime you install. It coordinates the machines you already trust into one private fabric, keeps the cheap, fast answers local, and only reaches a frontier model when the work genuinely needs one — through a key you own, on a connection we never sit inside.
Open-weight models load on your own GPU. A tiered router answers most requests from local memory, cache, and small models — the big model is the last resort, not the default.
When a request truly needs a frontier model, Sfiniti calls your provider with your key, directly from your machine. We are never in the path. No reselling, no pass-through, no markup.
Sfiniti holds no keys, no tokens, no prompts. There is no server of ours for your data to sit on. What runs on your machines stays on your machines.
Every request enters a ladder. Most never wake the large model — they're answered from your own memory, a cache, or a small model whose draft gets verified. Only the hard ones escalate.
The result: most prompts are answered for the cost of a lookup. The expensive model runs only when nothing cheaper will do — and when it's a frontier model, the call goes from your machine to your provider on your key.
One model can stay loaded while compatible work batches together. When a job outgrows one machine, bounded capacity routes can use other trusted devices. Speed and concurrency depend on the model, hardware, queue, and measured route; capacity is not presented as a universal speedup.
Run compatible agent requests through one loaded model instead of loading a model per agent. The measured benefit is workload- and hardware-dependent.
Shared model · bounded batchingKeep older context available through approved pooled storage instead of silently evicting it. This is an opt-in continuity and capacity feature, not a blanket speed claim.
Context retention · exact fallbackA bounded split-model preview can divide supported models across trusted devices when no one machine has enough room. It remains an advanced capacity route with explicit setup and fallback.
2Big2Fit · advanced previewWatch every machine in your fabric in real time: what is loaded, how much memory each is using, and which tier answered each request. Product telemetry stays local by default.
Per-machine GPU viewPoint Sfiniti at the providers you use. Keep the cheap, repetitive calls local; spend your frontier budget only on the calls that earn it.
Healthcare, legal, finance — where "we sent your data to a third party" isn't an option. Sfiniti keeps prompts and keys on machines you control.
Pool the GPUs already sitting in the office into one fabric, run bigger models than any single box could, and skip the per-seat cloud bill.
The free closed-source MFabric beta lets a trusted machine join your fabric and help with approved work. There is no Sfiniti account, no mandatory cloud, and discovery stays on your own network.
Get the MFabric beta → Get release updatesFor teams running AI where privacy and fidelity actually matter.