What is local AI? Models on your own infrastructure

Timo Wevelsiep•Updated: 23.07.2026

Editorial note: Versions, commands and prices may change. Please verify critical steps independently before production use. This guide does not replace individual consulting.

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"Local AI" has quickly gone from a niche topic to a serious path for companies. The core is simple: instead of sending requests to a cloud service, the language model runs on your own infrastructure. This article explains what that concretely means, why companies take this path and when it makes sense. As of July 2026.

Cloud AI or local AI
What "local" actually means
Why companies run it locally
What belongs to local AI
When local AI makes sense

Cloud AI or local AI

Most people know AI through cloud services: you type a question, it goes to a provider's servers - usually in the US - and the answer comes back. Convenient, but the inputs leave your own building in the process.

Local AI reverses this. An open language model runs on your own hardware, and the requests stay under your control. For users it feels similar - a chat interface, answers in seconds. The difference is underneath: where the model computes and who sees the data. That is exactly where data protection, cost model and independence are decided.

What "local" actually means

"Local" does not necessarily mean "in your own server room". What is meant is: on infrastructure you control. That can be:

a GPU server in your own data center or server room (on-premise),
a virtual machine on Proxmox or bare metal,
a server at a European hoster that is under your control.

What is decisive is not the location alone but that the models run under your authority and the data is not transmitted to an AI provider. The opposite pole is the cloud AI API, where a foreign company holds both model and data.

Why companies run it locally

Three reasons drive the switch:

Data protection and sovereignty - contracts, personnel files, design data or client information do not leave the building. No cloud provider processes them, no third-country transfer arises. This is the basis of GDPR-compliant AI and the core of AI sovereignty.
Cost control - no usage-based token fees that scale with success. With continuous use your own hardware becomes plannable and often cheaper.
Independence - no lock-in to the price, model or license changes of a single provider. You decide which model runs when.

The trade-off in detail - when cloud, when your own hardware - is shown in Cloud AI vs. self-hosted.

What belongs to local AI

Local AI is rarely a single tool but an interplay:

Hardware - usually a GPU with sufficient VRAM; small models also run on CPU.
An open model - powerful families like Llama, Qwen, Mistral or DeepSeek.
An operating stack - inference, gateway, observability and interface, see The open-source LLM stack.

Only this interplay turns a running model into a productive, operable system - including knowledge systems (RAG) on your own documents.

When local AI makes sense

Local AI pays off especially when at least one of these applies: you process sensitive or regulated data (law, health, public sector, industry), you have continuous, high usage where token costs weigh in, or you want to be independent of a single US provider.

For sporadic, uncritical use a cloud service can remain the simpler entry. But as soon as data protection, costs at scale or sovereignty matter, local operation is the more dependable path - and with today's open-source ecosystem it is realistically achievable for companies.

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Frequently Asked Questions

Answers to the most important questions

Local AI means the language models run on your own infrastructure - on a GPU server, on Proxmox or bare metal - instead of via a cloud API at an external provider. The requests and data do not leave your own building. It is also called on-premise AI or self-hosted AI.

With cloud services like ChatGPT you send your inputs to the provider's servers, usually in the US. With local AI an open model runs on your own hardware; the data stays under your control. Technically the usage is similar - the difference is where the model computes and who has access to the data.

For production operation of larger language models you usually need a GPU with sufficient VRAM. Small models also run on CPU or modest hardware. The right sizing depends on model size, desired throughput and number of users - from a single GPU server to a small cluster.

Local AI is the basis for GDPR-compliant AI because the data does not leave your building and no transfer to third countries takes place. GDPR compliance depends not only on the operating location, though, but also on access control, logging and contracts. Local operation, however, removes the fundamental problem of transferring data to external providers.

That depends on the usage profile. Local AI incurs acquisition and operating costs for the hardware but no usage-based token fees. With low, sporadic use cloud can be cheaper; with continuous, high load your own hardware often pays off quickly - in addition to the control and data protection advantage.

Not the closed models of the big providers, but powerful open model families like Llama, Qwen, Mistral or DeepSeek. For many enterprise tasks these achieve comparable quality and can be fully self-operated - the core of local AI.