local execution

in English: local deployment / on-premises

basicspreading

In local execution, the AI model runs on your own computer or your company's own server, not in a remote data center of an external provider.

In plain terms

You use most AI services like a restaurant: you place the order (your question), the kitchen works somewhere else, and you receive the finished dish. Local execution is home cooking: the kitchen is yours, the ingredients do not leave the house, but you must buy the stove, and the washing up remains your responsibility.

In practice, this means the model files are on your laptop, your company's server room, or a self-managed machine. Your questions and uploaded documents do not travel to an external company, and the system can operate without internet.

It is chosen especially where sensitive data is handled: in hospitals, banks, law firms, and public administration. The price is that locally run models are mostly smaller and weaker than the largest cloud models, and their operation requires hardware, electricity, and expertise.

How it works

First, you choose a freely downloadable so-called open-weights model whose license permits use on your own machine.
You download the model to your own computer or server; it is a large file, typically ranging from a few gigabytes to several hundred gigabytes.
A runtime loads the model into memory, preferably onto a powerful graphics card (GPU), because calculations are much faster on it.
The machine processes your question locally, the answer is generated there, and nothing leaves your network unless you connect it to an external service yourself.
Updates, security settings, access management, and troubleshooting are your responsibility, or that of your company's IT staff.

An everyday example

A general practitioner's office uses a program that drafts a medical report from the conversation, but the audio recording and text remain on the office's own computer. The same happens on a smaller scale when your phone can transcribe dictated text or search for photos even in airplane mode: the model runs on the device.

Who is affected

entrepreneur, executiveYou must decide whether to share trade secrets and customer data with an external provider or invest in your own hardware and operations.

developerIf you run a coding agent or other AI tool with a local model, the source code never leaves your machine; in return, you must handle the installation and performance tuning. → Code

patient, doctorHealthcare data is especially protected, so many institutions choose AI running within their own walls, or internal rules require it. The law generally does not ban the cloud, but strict contractual and security conditions must be met there. → Health

lawyer, citizenLocal execution facilitates compliance with data protection and confidentiality rules because it is precisely known where the data is and who has access. → Law

A common misconception

MythWhat runs locally is automatically secure and free.

In realityThe local system can be hacked or misconfigured, and the hardware, electricity, and specialist represent significant costs. The only certainty is that data does not automatically go to an external provider; if you connect the system to the internet or external devices, this is no longer true.

Limits and risks

Models runnable on your own machine are generally smaller and may perform less effectively on complex tasks than the largest cloud-based models.
Powerful hardware is required: a serious model needs an expensive graphics card or server, and this becomes obsolete within a few years.
The responsibility for security, updates, and backups lies entirely with you or your company; there is no provider to perform these tasks for you.
If you connect the local model or the agent built on it to internet search and external tools, data can leak out just as with cloud solutions.

Similar, but not the same

cloud-based execution – There, the model runs in the provider's data center; you access it only via the internet and pay based on usage or subscription.
open-source model – This describes the terms on which a model can be downloaded and used, not where it runs: an open model can also be run in the cloud. In everyday usage, open-weight models are often called open too, although with them only the finished model is available, and the license may restrict its use.
private cloud – There, machines serve only one organization but distribute resources flexibly in a cloud-like manner. They can be located on-site, but they are often housed in an external company's data center and operated by that company.

Related concepts

Broader

AI infrastructure, installation, operations

Narrower

on-device AI, air-gapped system

On the edge

cloud-based execution, open-weight model, small language model, data sovereignty, GPU, edge computing

A brief history

The term 'on-premises' became widespread in the 2000s, when the emergence of cloud services required distinguishing software running on-site. Local execution of language models became mass-accessible in 2023, when the first truly usable, freely downloadable models and runtime programs working on average computers appeared.

Where things stand

At the end of 2026, smaller models already work well on laptops and phones, and AI running on an organization's own servers is increasingly common in regulated sectors. Most users, however, still access the most capable models from the cloud.

Status: 2026 November

In the news

Appears in 5 Agent Age stories; the curve shows weekly occurrence in the news magazine.

Else Kröner Fresenius Center for Digital Health presents on-premise medical AI agent for clinical decision supportSep 15, 2026 · Health
Coder Agents becomes generally available, offering fully self-hosted AI codingSep 9, 2026 · Code
GitLab Duo Self-Hosted now supports bringing your own model through Microsoft FoundrySep 8, 2026 · Code
Northeast Ohio hospitals test AI to improve care and cut costsAug 28, 2026 · Health
Claude Code now available with a self-hosted runner for Docker and GitHub ActionsAug 18, 2026 · Code

Also known as

on-premises · on-prem · on-premise · local deployment · local execution · local installation · self-hosted · self-hosted AI

Sources

NIST SP 800-145: The NIST Definition of Cloud Computing

This entry was machine-edited and has undergone machine expert review; the “In the news” section updates automatically from Agent Age stories.