When someone talks about AI today, they almost always mean cloud services: ChatGPT, Claude, Gemini. These are interfaces that process your queries on remote servers, in data centres owned by companies like OpenAI, Anthropic or Google. That means your data travels, you depend on a stable internet connection, and access is subject to terms of service that can change. There is another option, less well known and more technical: running the model directly on your own computer. This is what is known as a local model.
What a local model is and how it works
A local language model is, simply, a model that runs on your own hardware. There is no external server, no API, no network. The text you write is processed inside your machine and the response is generated there.
This is possible because language models are, at their core, files of numerical parameters. A large model like GPT-4 has hundreds of billions of parameters and requires extremely specialised hardware. But over the last two years, smaller models have emerged, optimised to run on conventional hardware: laptops with 16 GB of RAM, desktop PCs with mid-range GPUs. Models like Llama 3, Mistral, Phi or Gemma can run on machines many people already own.
The most popular tool for managing local models is called Ollama. It lets you download, install and run models with a couple of commands, and exposes a local interface that mimics the OpenAI API, making it easy to integrate with other applications. It is not the only option — LM Studio and GPT4All are alternatives with friendlier graphical interfaces — but it is the most widely used among those who want something functional without giving up control.
The real advantages: privacy and control
The primary reason someone chooses a local model is privacy. When you use a cloud service, your prompts and the text you submit may be used to train future models, unless you have disabled that option or hold a subscription that contractually guarantees otherwise. This matters particularly in professional contexts involving confidential information: contracts, client data, internal strategy. With a local model, nothing leaves your machine.
There are other advantages that get less attention. Independence from connectivity is one: a local model works without internet, which can be decisive in environments without reliable coverage or where guaranteed availability is essential. There are also no usage limits imposed by a provider, no queues during peak demand, and no prices that rise with token volume. Once a model is downloaded, the marginal cost of each query is zero.
Control over configuration is another argument. You can adjust parameters like temperature, context length and output format with a level of granularity that cloud services do not always permit. And you can use the model inside automated workflows without depending on external quotas or changes in provider policy.
The limitations nobody mentions
Local models have clear disadvantages that are worth understanding before investing time in the setup.
The first is the quality gap. The best models available for local use — those that can run on conventional hardware — are significantly less capable than frontier cloud models. A 7 billion parameter model does not compare to one with 400 billion. For complex reasoning, nuanced analysis, long coherent text generation, or interpreting ambiguous instructions, cloud models remain superior by a considerable margin.
The second is hardware. For a smooth experience, you need at least 16 GB of RAM and, ideally, a GPU with sufficient memory to load the model. On a mid-range laptop, a 7B parameter model may respond at tolerable speeds, but slower than any cloud service. Larger models — 13B or 70B parameters — require more powerful machines or sacrifice speed noticeably.
The third is continuous updates. Cloud models improve constantly without the user having to do anything. A local model downloaded six months ago may be outdated relative to new versions, and updating means downloading files of several gigabytes, managing versions and, in some cases, reconfiguring integrations.
Finally, the setup curve. Although tools like Ollama have greatly simplified the process, the initial configuration is still more demanding than creating an account on a web service. It is not for everyone, and the time required does not always justify the benefit.
When it actually makes sense
There are scenarios where the balance clearly tilts towards local models.
The first is working with sensitive data. If you handle confidential medical, legal or financial information, and your organisation cannot guarantee data processing agreements with a cloud provider, a local model may be the only legally viable option. This is not about convenience — it is about regulatory compliance.
The second is automating repetitive and simple tasks. For tasks that do not require sophisticated reasoning — classifying texts, extracting structured information, generating fixed-format responses — a local model of modest quality can be perfectly adequate, especially if volume is high and API costs would be significant.
The third is learning and experimentation. If you want to understand how language models work from the inside, experiment with parameters, build your own applications or simply explore capabilities without worrying about spending, a local model offers a freedom that usage-billed services do not.
The fourth is availability without a network connection. Journalists in conflict zones, researchers in the field, professionals in restricted connectivity environments: there are contexts where depending on the cloud is simply not viable.
How to get started without technical expertise
If you want to experiment without committing to a complex setup, the most sensible starting point is Ollama paired with a graphical interface like Open WebUI. Installing Ollama takes a few minutes, and from there you can download models with a simple command. Mistral 7B or Llama 3 8B are good starting points for a machine with 16 GB of RAM.
If you prefer a more visual experience from the start, LM Studio has a graphical interface, runs on Mac, Windows and Linux, and lets you browse and download models from a catalogue without touching the command line.
The practical recommendation is to start with a specific, well-defined task rather than an open-ended question. Ask the model to classify texts into categories, summarise short documents, or generate responses from templates. That way you can evaluate whether quality is sufficient for your needs before integrating the model into your existing workflow.
Local models are not the future of AI for general use. But they are a useful tool and, in some contexts, the only correct one. Knowing when to use them and when not to is part of using AI with genuine judgement.
