Can Your Linux PC Run Local AI? Real Benchmarks on Modest Hardware

Want to try AI on your own computer? You probably don't need a flashy new workstation. A regular Linux PC can run some models locally. The catch: a model that fits in memory might still answer slowly.

Ollama, LM Studio, and llama.cpp all let you run models on your own hardware, but they make different parts of the job easier. One gives you a straightforward terminal workflow, one gives you a desktop app, and one gives you more control over the engine.

We looked at published benchmark results to get a feel for what modest hardware can do. We didn't run these tests ourselves, and the numbers below come from different systems and setups. Think of them as examples, not a promise about how fast your machine will be.

What do the benchmarks look like?

Tokens per second, or tokens/s, is one way to describe how fast a model generates text. More tokens usually means more text, but a token isn't always a whole word.

The first number comes from ComputingForGeeks' July 2026 test. The Pi results are from Jeff Geerling's January 2025 benchmark. The laptop result comes from a llama.cpp community benchmark discussion.

These aren't apples-to-apples results. They use different computers, models, and software settings. The Pi results, for example, identify model tags without pinning the model files and quantization in the table. The laptop tester also changed the thermal setup to avoid throttling. Treat each as one person's result on a particular setup, not a speed guarantee for all Linux PCs.

Still, there's a useful takeaway: smaller models can run on modest hardware, but speed can drop quickly as the model gets bigger. In the Pi results, the 3B model generated more than twice as quickly as the 8B model.

Generation speed isn't the whole story either. The computer also needs time to read your prompt before it starts answering. A long document can take noticeably longer to process than a short question, even if both use the same model. The llama.cpp project reports prompt processing separately from generated text in its benchmark tool.

How much computer do you need?

For a first try, start with a small model and a short conversation. A 3B model is a reasonable starting point on a 16 GB computer, especially if you don't have a dedicated graphics card.

A 7B or 8B model may also fit, depending on the model file, context length, and what else is running. A bigger model can give you a different experience, but it can also take longer to respond.

Quantization helps shrink a model so it uses less memory. You'll see labels such as Q4 in model names. These describe how the model is stored; they don't tell you exactly how much total memory it will need while running.

Leave memory for Linux and your other apps, too. Longer conversations need more memory because the model has more text to keep in view. Ollama's context guide explains how context size affects memory use.

Ollama: get going from the terminal

Ollama is a good fit if you're happy typing a few commands and want other apps to connect to your model.

For example, you can start a chat with a 3B Qwen 2.5 model like this:

ollama run qwen2.5:3b

That model is listed in the Ollama library. Ollama's Linux guide covers installation, running it as a service, and optional GPU setup.

Once the model is running, use ollama ps to see whether it's using the CPU, GPU, or both. Don't assume your graphics card is doing the work until you've checked. Ollama's FAQ explains what the output means.

Pick Ollama if you like the terminal and may want your local model to work with other software.

LM Studio: chat with a desktop app

Prefer clicking through a model list and opening a chat window? LM Studio gives you a desktop interface for finding, downloading, and loading models. Its getting started guide walks through the process.

Check its Linux requirements before you install it. LM Studio distributes its Linux app as an AppImage, and its documentation flags newer Ubuntu versions as not well tested. You can check the current details in its system requirements.

Once you've downloaded a model, LM Studio says you can chat with it offline. Searching and downloading models still need an internet connection. Its offline guide explains what does and doesn't need to be online.

Pick LM Studio if you want to explore models and chat without making the terminal part of your daily routine.

llama.cpp: when you want more knobs

llama.cpp is the inference engine behind many local AI setups. It runs on CPUs, supports several GPU backends, and lets you adjust how much work runs on each. The project's documentation has the details.

That control is handy if you want to choose a particular model file or tune which hardware does the work. It also means you'll need to make more choices yourself.

You can find a lot of llama.cpp numbers online, but compare the details before deciding one computer is faster than another. The project's Vulkan benchmark thread warns that drivers, operating systems, and memory speed can all affect the result. Integrated graphics share system memory, so memory speed and setup matter too.

Pick llama.cpp if you enjoy tuning software and want direct control over the model and hardware.

So, where should you start?

If you have 16 GB of RAM and no dedicated GPU, start with a small model and keep your first prompts short. Try Ollama if the terminal feels fine, or LM Studio if you'd rather use a desktop app.

Give the model a simple task, like rewriting a paragraph or explaining a short piece of text. If the response takes too long, try a smaller model before changing everything else.

llama.cpp is there if you want to take control of the setup. You don't need to begin with its many settings just to find out whether local AI is useful to you.

A modest PC can run local models. The trick is picking a model that fits both your memory and your patience.