Quick wins for a faster PC:
Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →To run an open-weight AI model on your computer, install a local model runner, download weights that work with it, load the model into memory, and start a chat. For a guided desktop setup, use LM Studio; for a terminal-based workflow, use Ollama. Choose a model and context size that suit your hardware, and check the model’s license before using it.
Choose a local model runner
A runner provides the software interface that downloads or loads model weights and runs inference on your computer. These are the practical starting points:
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| Runner | Best fit | What it supports | Trade-off |
|---|---|---|---|
| LM Studio | First-time desktop setup | Discover and download models, load them, and chat in a graphical interface. It supports GGUF through llama.cpp and MLX on Apple Silicon. See the LM Studio getting started guide and LM Studio documentation overview. | The visual workflow is guided, but you still need to choose a compatible model and have enough memory. |
| Ollama | Terminal use or application integration | Installers are available for macOS, Linux, and Windows. Run models by name, use its local API, or import a compatible GGUF artifact. See Ollama downloads and its GGUF import instructions. | Commands are direct, but you need to select the right model tag and account for hardware fit. |
| llama.cpp | Users who want a lower-level GGUF runtime | LM Studio identifies llama.cpp as its GGUF engine across supported desktop platforms; see the LM Studio documentation overview. | It offers a less guided route. The documentation cited here does not provide a complete compile-from-source tutorial. |
Check your computer before downloading
Model weights and runtime state use memory. A longer context—the amount of conversation or input the model can consider—can add to resource needs, as can other load settings. A model file that downloads successfully may still be too large for a machine to run comfortably.
LM Studio’s current system requirements recommend 16GB or more of RAM. Its guidance says an 8GB Apple Silicon Mac may work with smaller models and modest context sizes, and recommends at least 4GB of dedicated VRAM for Windows. These are LM Studio recommendations, not universal minimums or guarantees for every model.
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Ollama’s 2025 model-specific guidance says its gpt-oss-20b MXFP4 model can run on systems with as little as 16GB of memory; it also says gpt-oss-120b fits on a single 80GB GPU. Those figures apply to the stated Ollama models and formats, not to every model with a similar parameter count. See Ollama’s gpt-oss announcement.
- If memory is limited, start with a smaller model and modest context length.
- Check the model’s required format and the runner’s compatibility before downloading.
- Expect speed to vary with the model, quantization, context, runtime, CPU or GPU, and available memory. There is no reliable universal speed figure for all local setups.
Run a model with LM Studio
LM Studio is the most visual route for a first run. Its documentation says the app is available for macOS, Windows, and Linux. Follow this workflow:
- Install LM Studio for your operating system, then open the app.
- Open Discover, search for a model, and download a version compatible with your computer and the runner.
- Open the model loader and select the downloaded model. Adjust load settings if needed, keeping your available memory and intended context size in mind.
- Open the Chat tab and send a prompt. The model runs locally after its weights are loaded.
LM Studio describes loading as allocating memory for the model’s weights and other parameters. The weights must therefore fit alongside the runtime’s other memory needs; downloading a model alone does not mean it is ready to answer prompts.
Run a model with Ollama
Ollama provides a command-oriented path. Install it using the Ollama download page, then run a model by its available name and tag. For example, the vendor documents ollama run gpt-oss:20b. Model names and tags can change, so check Ollama’s current listing if a command is unavailable. Ollama also supports a local API for applications that need to connect to a running model.
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Import a particular GGUF file
If you need a specific compatible GGUF artifact rather than a model from Ollama’s library, Ollama’s article published June 5, 2026 describes this workflow:
- Download the GGUF file or its containing directory.
- Create a file named
Modelfilewith aFROMline pointing to the GGUF file. - Run
ollama create -f Modelfile my-model. - Start it with
ollama run my-model.
Follow Ollama’s GGUF instructions for the exact file and Modelfile requirements.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose compatible weights and check the license
Local execution requires access to model weights. They are commonly distributed as .gguf or .safetensors files, but a file format must be supported by the runner you choose. LM Studio uses llama.cpp for GGUF models and supports MLX on Apple Silicon; consult the model and runner documentation rather than assuming any downloaded artifact will work everywhere.
“Open-weight” does not necessarily mean open-source or unrestricted. LM Studio notes that models described as open-weight or open-source have varying degrees of openness and different licenses. Read the chosen model’s actual terms, especially before commercial deployment, redistribution, or use with sensitive data.
Quick Recap
If the model does not run as expected
- The model will not load: Confirm that the weights are in a format your runner supports and that the selected model fits available memory. Try a smaller model or reduce the context size.
- The run command fails: Check the exact model name or tag in the runner’s current catalog. For an imported GGUF, verify that the Modelfile’s
FROMpath points to the downloaded artifact. - Responses are slow: Performance depends on the model, quantization, context length, runtime, processor, and memory. Try a smaller model or shorter context before changing hardware.
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