Private, local-first AI runtime

Your AI runs on your machines.
Your keys never leave.

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.

● local-first · ● bring-your-own-key · ● no data resold, ever
The pivot

Most AI tools put a company between you and the model. We took it out.

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.

01 / LOCAL-FIRST

The model lives on your hardware

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.

02 / BRING YOUR OWN KEY

You own the frontier connection

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.

03 / SOFTWARE, NOT A SERVICE

We never hold your data

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.

Runtime direction

A ladder that spends the cheapest verified answer first.

Sfiniti's private runtime work is organized around a measured routing ladder. Individual tiers stay off until their own quality gates pass; the current public MFabric download keeps the normal exact path as its default.

01
Memory
Answered from what your fabric already knows.
instant
02
Cache
Semantic match — with an entity guard so look-alikes never collide.
near-instant
03
Fact & freshness
Stable facts served locally; anything time-sensitive always goes live.
local
04
Draft & verify
A small model drafts; the large one checks it in a single pass and accepts when confident.
small model
05
Escalate
The loaded model, a model split across your machines, or your own frontier key.
the model

The rule: promote a cheaper route only when it preserves the required result and proves a visible benefit. Otherwise the request stays on the exact/default path.

The swarm

Concurrent agents. One shared model. Your machines as one fabric.

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.

shared concurrency

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 batching
long context, small footprint

Keep 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 fallback
many machines, one model

A 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 preview
live telemetry

Watch 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 view
Who it's for

For people who'd rather not hand their work to a black box.

Developers

You already have keys

Point Sfiniti at the providers you use. Keep the cheap, repetitive calls local; spend your frontier budget only on the calls that earn it.

Privacy-first teams

Regulated & sensitive work

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.

Small teams & labs

Make the hardware you own count

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.

Also in the lab

Small, inspectable interfaces around expensive work.

MFabric remains the product front door. These adjacent projects are visible only where a public artifact or a carefully bounded design-partner path already exists.

Public review beta

CodeMani

Package one bounded code change as a fingerprinted work order, then verify its source, precomputed result, and oracle. The private materializer is not included.

Review the packet →
Interactive preview

ManiMotion

Describe supported motion, inspect the admitted constraints, and keep the result as a compact program that replays without being generated again.

Try the motion synthesizer →
Design-partner work

Governed agent writes

Let an agent propose a bounded infrastructure change, check it against policy, and produce a verifiable receipt. Current proof is limited to exercised non-production paths.

See the bounded demo →

Private native acceleration is being developed behind exact, fail-closed product interfaces. Internal components are not separately offered or described here.

Public beta

Run a piece of it today.

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 updates
Local install · runs on your network · your data never leaves it
Need help?

Talk to us.

For teams running AI where privacy and fidelity actually matter.

[email protected]