What is local AI?

• The weights are on your hardware: You hold the model file itself, not an API key that grants access to someone else's copy.
• Inference happens in your network: Prompts, documents and outputs are processed on your machine and never transit the public internet.
• It works offline: Because nothing is called remotely, a local deployment keeps running air-gapped or during an outage at a provider.
• Cost is fixed: You pay for the hardware once. Usage does not add to the bill, so teams can adopt AI heavily without a growing invoice.

For most of the current AI wave, running a competitive model locally was not realistic: the best models were far too large for anything short of a data centre. Two things changed. Open-weight models from labs like DeepSeek, Qwen and NVIDIA closed much of the gap to the closed frontier, and compression techniques improved enough to fit those models onto a small number of GPUs without gutting their quality.

The result is that a single appliance can now serve a whole team with a model that would have needed a rack two years ago. At Locai we push this further with SPACE, a compression algorithm that asks what a model needs to be good at, preserves the subnetworks behind those capabilities and strips back the rest, producing a smaller specialist rather than a shrunken generalist.

• GPU hardware with enough VRAM: Model size and precision determine the requirement. A quantised mid-size model needs tens of gigabytes; a large one needs hundreds.
• An inference engine: Software to serve the model efficiently to many users at once, rather than one request at a time.
• An access layer: An API, a chat application, and control over who can use which model.
• Somewhere to put it: Standard mains power and a network port. Modern appliances are air-cooled and quiet enough for an office.

These terms overlap heavily and are often used interchangeably, which causes real confusion for buyers. Each emphasises a different aspect of the same arrangement: local AI describes where the compute is, on-prem AI describes where the deployment sits, private AI describes what happens to your data, and sovereign AI describes who holds legal and jurisdictional control.

In practice, running AI locally on hardware you own delivers all four at once. That is the point: privacy and sovereignty stop being contractual promises and become properties of the architecture.

Local AI suits sustained, sensitive, high-volume work: engineering teams running coding agents all day, analysts working through confidential documents, research groups whose most valuable knowledge cannot leave the organisation. The economics favour it precisely when usage is heavy, because that is when per-token billing hurts most.

It is a weaker fit for spiky, occasional experimentation, or when you need a specific proprietary model that is only available hosted. Being honest about that matters: the question is not whether local beats cloud in the abstract, but whether your workload is steady enough to justify owning the machine.

Locai One is an on-prem AI appliance that packages the three layers together: the hardware, our open-weight Locai Juno models, and Locai OS, which serves the models and handles users, access and monitoring. It plugs into a standard socket and your network, and is running in around 15 minutes. Any compatible open-weight model can run alongside Juno, and organisations that need a model trained on their own data can have one post-trained and deployed on the same machine.