The Tool Desk
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What does “run locally” mean?
Local inference means the model’s weights are executed on infrastructure you control rather than sent to a hosted model API. That can give you more control over where inference runs, but it does not make the compute or storage free: your computer still needs to hold the model and perform the work.
“Open-weight” does not automatically mean unrestricted use. Check the exact model card, license, and any usage policy before downloading. For example, OpenAI describes its gpt-oss weights as Apache 2.0, subject to a usage policy; it also says gpt-oss is not available through the OpenAI API or ChatGPT. Those terms and availability are specific to gpt-oss, not all open-weight models. OpenAI’s gpt-oss announcement
What do you need to run a language model locally?
There is no universal RAM or VRAM minimum for local language models. Requirements depend on the model, its size and quantization, the runtime, and your computer. Ollama cautions that speed depends on hardware and that large models can be slow without a strong GPU. Ollama’s download page
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Before choosing a model, identify:
- Your operating system and whether you are comfortable using a terminal.
- Available system memory and GPU capability.
- The task you want the model to handle.
- The model’s supported runtime, file format, license, and recommended quantization.
Do not treat a published requirement for one model as a general minimum for local inference. Match the specific model to your machine and check its publisher’s current instructions.
Which local inference runtime should you choose?
| Runtime | Best fit | Format and workflow |
|---|---|---|
| Ollama | A relatively approachable installation and model workflow | Install for your operating system, then follow the chosen model’s current Ollama instructions. |
| llama.cpp | Users who want a command-line workflow or an HTTP server interface | Requires GGUF models; supports Hub-hosted compatible models and models already stored locally. |
Ollama: the simpler starting point
Ollama provides installation routes for macOS, Linux, and Windows. Its download page currently shows these commands; verify the official page for your operating system before running them because install instructions can change:
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- macOS or Linux:
curl -fsSL https://ollama.com/install.sh | sh - Windows PowerShell:
irm https://ollama.com/install.ps1 | iex
After installing, use the current instructions for the particular model you want. The download page is not a stable source for a universal model-run command, so do not assume that one command or model tag applies to every model. Download Ollama · Ollama
llama.cpp: more explicit control
llama.cpp is a C/C++ inference engine for local deployment. Its documentation says it does not require Python or CUDA, and it uses GGUF model files. That makes a CPU-only setup possible, though it does not ensure that a particular model will fit or run at a useful speed. Hugging Face Transformers documentation: GGUF and llama.cpp
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llama.cpp can run compatible models from a Hub repository or from local storage. It also includes llama-server for an HTTP server interface. Choose it when you want to work directly with model files and command-line options rather than rely on a higher-level model workflow. llama.cpp project
How do you choose a compatible model?
Start with the model publisher’s card and confirm the intended use, supported runtime, file format, license, and recommended quantization. A model’s presence in a catalog does not by itself establish that every runtime supports its architecture or features.
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For llama.cpp, select a GGUF artifact or follow the project’s documented conversion path if you are starting with another supported format. GGUF supports quantized weights and memory mapping. Quantization can reduce the weight footprint, but there is no reliable general promise about the resulting quality, speed, or memory use: those vary by model and machine. llama.cpp documentation and project
The llama.cpp documentation describes this pattern for a compatible Hub model:
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llama-cli -hf <user>/<model>[:quant]
Hugging Face’s llama.cpp integration page gives llama-cli -hf ggml-org/gpt-oss-20b-GGUF as an example. Treat it as an example, not a guarantee that the repository name, available quantizations, or compatibility will remain unchanged. Check the selected repository and current project instructions. Hugging Face Transformers documentation: GGUF and llama.cpp · llama.cpp project
How to install and run a model
- Check your machine and task. Note your operating system, available memory, GPU, and what you need the model to do. Use the model publisher’s recommendations rather than a generic hardware threshold.
- Choose a runtime. Install Ollama using its current instructions for your operating system, or set up llama.cpp using the project’s current documentation.
- Select a model and verify its terms. Confirm the exact model’s supported runtime and format, then review its license and usage policy.
- Download and run using the model’s current instructions. For llama.cpp, use a compatible GGUF model with
llama-clior run an already downloaded model from local storage. Usellama-serverif you need its server interface. For Ollama, follow the chosen model’s official run instructions rather than guessing a tag. - Try the model on your actual task. If it is too slow or will not load, check the fit, format, and runtime support before switching models or quantizations.
Can you run a local model without a GPU?
Yes, some local inference paths can run without a GPU: llama.cpp does not require CUDA. That is not a promise of good performance for every model. Ollama notes that large models may be slow without a strong GPU, and the specific result depends on the model and computer. If your first choice is impractical, try a smaller model or a supported quantized version and compare the output quality on your intended task.
Quick Recap
What to check if a model is slow or will not load
- Memory fit: Check whether the selected model and quantization are suitable for the memory available on your machine.
- Format support: For llama.cpp, verify that you have a compatible GGUF file.
- Architecture support: Confirm the runtime supports the model architecture and the features you plan to use.
- Model choice: If the model is too large for a practical run, try a smaller option or an available quantization supported by the runtime.
- Current instructions: Recheck the model repository and runtime documentation if a command, model tag, or download path no longer works.
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