Turn end-user devices into sovereign off-cloud AI infrastructure
Orchestrate local models across user devices from a central control plane. On-device first — route to on-prem or cloud when you need to. OpenAI-compatible API.
Backed by
Simple
Turn user devices into AI inference infrastructure with a single command. OpenAI-compatible API — no code changes required.
Scalable
Unlike centralised cloud or big-server setups where everyone connects to one place, on-device inference scales instantly with every new user.
Cost-Efficient
Move repeated, high-frequency workloads to user devices. Heavy or rare jobs route to your own servers or cloud — you decide where each runs.
For devs and teams that are building
real-time AI features where API latency breaks UX
products where user data must stay on the device
workloads that can run on local models vs paid APIs
AI features where inference cost scales with usage
applications that must run without stable connectivity
Most AI-powered product features don't need frontier models
Local models are viable for many product features — once validated and adapted in real end-user environments. Loc.ai lets you do that without manual setup or per-device configuration, from a single control plane.
What you can run locally
Supported models
How you run it with Loc.ai
Run models on real user devices, not dev machines
Deploy and test models across hardware and environments from the control plane
Compare models in real product flows, not benchmarks
Choose the right model and optimisation strategy based on real usage data
Adapt or fine-tune models and roll them out across all user devices from one place
Request a personal demo to get Loc.ai evaluated on your fleet
Request a demoDeploy on-device AI infrastructure in minutes
We provide the full on-device AI infrastructure —completely application agnostic.
Install the runtime (Loc.ai:Link)
One curl command — handles dependencies, Python setup, device registration, and agent startup automatically.
Deploy a local model
Pick from the model library (GGUF, TFLite) or bring your own. Deploy to your entire fleet from the control plane.
Run & call via API
Runs on-device first, routes to your servers or cloud when needed. OpenAI-compatible API
const openai = new OpenAI({ baseURL: "http://localhost:8100" })Manage everything centrally
Register devices, push model updates, monitor inference results and device telemetry — CPU, RAM, temperature — all from Loc.ai:Control.
Loc.ai architecture
Device-first execution. Control plane orchestrates. Heavy or rare jobs route to your own servers or cloud — optional.
jobs optional
Built on a pub/sub architecture — the standard for distributed, low-latency systems. Powered by Zenoh, an open protocol for edge and robotics workloads.
Call local models via the OpenAI-compatible API
Point your existing SDK at a localhost endpoint—no code changes, no new client, no per-device setup.
Same API. Local execution.
Your inference cost no longer scales with usage
With local inference repeated and high-frequency workloads run on user devices, API usage is limited to tasks that actually require large models.
As a result cost per user becomes more predictable and margins don't shrink as usage grows.
Compare local vs API-based execution costs
See Loc.ai in action
See how to install, deploy, and run a model on a device