A language model can be run locally, meaning without the internet, without a subscription, and without sending your data to anyone externally. A few years ago, this required serious hardware and technical skills. In 2026, all you need to get started is a laptop with eight gigabytes of RAM and about five minutes.

Two Programs to Start With

There are two main tools, and the difference between them lies in the interface. The first works through the command line, meaning you enter text commands. It is free, open-source, and supports more than a hundred open models. The second is a regular program with a window and buttons, meaning that downloading a model and chatting with it are done using a mouse. It is also free, officially for both home and business use. Both work on Windows, macOS, and Linux. The logic behind choosing is simple. If the command line does not intimidate you, choose the first. If you just want to click buttons, choose the second.

How Much Memory Is Needed

This is the main limitation, and it is precisely what determines what you will be able to run. Models differ in the number of parameters, and this number affects both quality and hardware requirements. Models with approximately one to four billion parameters run on eight gigabytes of RAM. They are suitable for conversation, short summaries, and drafts, but are weak at complex tasks and coding. Models with approximately seven to fourteen billion parameters run on sixteen gigabytes of RAM, and this range is considered optimal in 2026. These are already full-fledged assistants that can handle text, many languages, and some coding. Models with approximately up to thirty billion parameters run with twenty-four gigabytes of video memory. With thirty-two gigabytes or more, models ranging from thirty-two to seventy billion parameters become available, and at this level, quality approaches that of cloud services. Relatively little disk space is required, meaning that a model with three billion parameters takes up approximately two gigabytes, one with eight billion takes about five, one with twelve billion takes about seven, and one with seventy billion takes about forty.

About Speed

Here is the parameter that determines whether you will actually use it. Speed is measured in tokens per second, and one token roughly corresponds to part of a word. On a modern desktop processor without a graphics card, a model with seven billion parameters generates approximately three to eight tokens per second. This means that an answer the length of a paragraph will take about a minute to generate. You can use it this way, but it is uncomfortable. With a dedicated graphics card, the situation is different: the same model generates approximately twenty-five to sixty tokens per second, which already feels like a normal typing speed. Apple computers with their own processors deserve a separate mention. Thanks to the unified memory shared between the processor and graphics core, they work with models noticeably better than their specifications might suggest, and a model with seven billion parameters generates approximately forty to fifty tokens per second on them. In other words, comfortable use requires either a dedicated graphics card or a recent-generation Apple computer, while on a regular office laptop, it will work but slowly.

What It Looks Like in Practice

You install the chosen program from its official website. You find the model you need in the built-in catalog and download it. You launch it and start chatting in the program window or through the command line. Once the model has been downloaded, the internet is no longer needed, meaning you can put your computer into offline mode and everything will continue to work.

What to Expect in Terms of Quality

A local model that fits on a regular laptop is noticeably weaker than what you use in the cloud. It reasons less effectively over multiple steps, handles the context of a long conversation less well, and makes up facts more often. The tendency to generate fabricated answers is more pronounced in smaller models, and I wrote separately about the mechanics of this phenomenon. In addition, the model's knowledge is limited by its training cutoff date, and it has no access to search, meaning it knows nothing about recent events. Therefore, expectations should be set accordingly. A local model running on sixteen gigabytes of RAM is a good assistant for working with text you have provided, explaining established concepts, drafting, and translation. It is not a source of facts and does not replace a full-fledged cloud service for complex tasks.

When It Really Makes Sense

Privacy is the main argument. Data never leaves your computer, meaning that working with medical, legal, or commercial documents offers a fundamentally different level of control. Working without the internet is the second argument, and it is significant in our circumstances because the model works the same way both online and offline. There are no limits on the number of requests because you do not pay for each query and can make as many as you want. Cost is another advantage because, after the initial download, you pay nothing. Learning and experimentation are also benefits because you can freely try different models and settings locally.

When It Is Not Worth It

If you need the highest possible answer quality, because cloud models remain significantly more capable. If you need up-to-date facts, because the local model does not know them. If you have eight gigabytes of memory and expect the performance of a cloud service, because disappointment is guaranteed. If you are running your laptop on battery power, because running the model puts a significant load on the processor and reduces battery life.

Security Warning

One technical note for those who go beyond simple conversation. If you connect a local model to tools, meaning you give it the ability to execute commands or work with files, you should understand the risk. Local models do not have the same filters that cloud services apply on their side, so they are more vulnerable to situations in which a malicious instruction is embedded in the text you have asked them to process. In practical terms, this means you should not give a local model broad permissions to act on your files. If you do, restrict it to a specific task.

Where to Start

Check how much RAM you have, because this determines everything else. Choose a program based on your interface preferences. Start with a small model to test its speed on your hardware, and only then move on to a larger one. Compare the results with what you currently use on your real tasks, rather than on test questions. Decide whether it is worth it after making this comparison, not before.

Summary

Running a language model on your own computer has become accessible, and getting started requires just eight gigabytes of RAM, free software, and a few minutes. The optimal setup in 2026 is sixteen gigabytes of RAM and models with seven to fourteen billion parameters, which provide a full-fledged assistant for working with text. Meanwhile, on a regular office laptop without a graphics card, the speed will be approximately three to eight tokens per second, meaning it is usable but uncomfortable. The main point is that a local model is not a free replacement for a cloud service, but a different tool whose advantages are privacy, offline operation, and the absence of usage limits. These are the criteria by which it should be evaluated.


Sources

Technical requirements and speed benchmarks from the LM Studio and Ollama documentation, industry reviews of local models from 2026, and data on model sizes and memory consumption.