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Ollama lets you download and run open AI models on your own Mac, Windows PC, or Linux machine. Install the app for your platform, choose a model that fits your available storage and hardware, then start it with a command such as ollama run llama3.2. The model runs locally, but that does not make every model small or fast: Ollama’s published examples range from 1.3GB to 55GB, and performance depends on your computer. This guide covers installation, model choice, storage, GPU checks, and the local developer API.
What is Ollama, and what does “run locally” mean?
Ollama is software for downloading and running open AI models on a computer you control. In its Linux documentation, Ollama describes two ways to run open models—locally on the computer or through other means—and says local speed depends on the hardware. In a local run, the model files are stored on your machine and Ollama starts the model there; it is not the same as sending each prompt to a hosted model service.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchOllama is the runner, not the model itself. You choose a model from its library, download the files needed for that model, then issue prompts through the command line or connect software to the local API. The distinction matters: Ollama’s installation footprint is only part of the storage requirement, and the model you select largely determines how much additional space and computing capacity you need.
The “free” part should be understood narrowly. Ollama’s documented workflow is for running open models locally, but the supplied documentation does not establish a universal pricing policy for every model, service, or related product. Local use also still depends on your own computer, electricity, and storage.
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How do I install Ollama on macOS, Windows, or Linux?
Ollama provides native installation paths for macOS, Windows, and Linux. Choose the platform-specific download or installation method, complete setup, and then run a model from a terminal or command prompt.
macOS
- Use the macOS download and application path in Ollama’s quickstart.
- After installation, open a terminal and start a model with
ollama run llama3.2. If it is not already present, Ollama will obtain the model files needed for the run. - Wait for the model to load, then enter a prompt. The model choice affects both the download size and the resources needed while it is running.
Windows
- Install Ollama’s native Windows application. The documented minimum supported version is Windows 10 22H2 or newer.
- For GPU acceleration, follow Ollama’s current driver and runtime guidance for your hardware. The Windows documentation identifies support paths for NVIDIA and AMD Radeon GPUs; support is subject to those requirements.
- Open a command prompt or terminal and run
ollama run llama3.2to fetch and start the model.
Ollama’s Windows documentation states that the application installation needs at least 4GB of storage. That is not a model-storage allowance: model files require additional disk space, potentially tens or hundreds of gigabytes in total if you keep multiple models.
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- Open a terminal on the Linux machine where you want to run Ollama.
- Run the official install command:
curl -fsSL https://ollama.com/install.sh | sh. - After installation, start a model with
ollama run llama3.2.
Ollama notes that local speed depends on the hardware, and that large models can be slow on a computer without a strong GPU. Linux users should check the current platform guidance for details relevant to their configuration.
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Which Ollama model will fit my computer?
Start with the model’s download size, then consider its capabilities, the memory available to run it, and how quickly you need it to respond. A model that fits on disk may still be too demanding for comfortable use on a particular computer.
| Ollama model-library example | Listed size | What the example illustrates |
|---|---|---|
| Llama 3.2 1B | 1.3GB | A comparatively small listed model |
| Llama 3.2 3B | 2.0GB | A larger text-model example |
| Llama 3.2 Vision 11B | 7.9GB | A vision-model example |
| Llama 3.2 Vision 90B | 55GB | A much larger vision-model example |
These are examples from Ollama’s 2024 documentation, not guaranteed current download sizes. Model tags and quantizations can change, so check the live model-library entry before downloading. The figures describe model-file size; they do not state how much RAM or VRAM a model needs to run.
A practical model-selection check
- Capability: Pick a model suited to the task, such as a text-only model for general text prompts or a vision model when image input matters.
- Storage: Confirm the model files fit on the drive where Ollama stores them. Leave space for other models you plan to keep; the total can grow into tens or hundreds of gigabytes.
- Memory: Compare the model’s demands with the RAM and, where applicable, VRAM available on your computer. The listed file size alone is not a memory specification.
- Speed: Decide whether a slower response is acceptable. Ollama says speed depends on hardware, and large models may be slow without a strong GPU; the documentation cited here does not provide comparable speed benchmarks for specific consumer computers.
If you are uncertain, begin with a smaller model, confirm that it runs acceptably, and then try a larger or more capable option if your available resources allow. This is a practical way to avoid committing substantial disk space to a model that is not a good fit.
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How much storage do Ollama and its models need?
Separate the space required by the Ollama application from the space used by model files. On Windows, Ollama documents at least 4GB for the application installation, while its documentation warns that language-model files may require tens to hundreds of gigabytes. The 4GB figure is not a total-space estimate for an Ollama setup.
If the internal drive is tight, Ollama documents the OLLAMA_MODELS environment variable for pointing model storage to another directory. An external USB SSD is one practical place to keep model files when you do not want them on the internal drive. Choose a destination with enough free space for the models you intend to download, and configure the storage location as described in Ollama’s current platform documentation.
Keep track of what you download. The model library’s displayed sizes can vary with model tags and quantizations, and keeping several large models means their files accumulate. Before starting a download, check the current entry and the free space on the destination drive rather than relying on an older size figure.
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How can I tell whether a model is using the GPU?
Run ollama ps while the model is loaded. Ollama’s output indicates whether the model is running with 100% GPU, 100% CPU, or split across CPU and GPU.
This check is useful when a model runs more slowly than expected. If the output shows CPU use or a split, compare it with your machine’s hardware and platform support requirements. On Windows, GPU acceleration depends on the supported NVIDIA or AMD Radeon path and the applicable driver/runtime requirements. A status line tells you where Ollama is placing the model; it is not a benchmark or a promise of a particular response time.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can I use Ollama from my own software?
Yes. Ollama exposes a local HTTP API at http://localhost:11434, and its project README identifies Python and JavaScript libraries for integration. Developers can use these options to connect their own applications to a model running on the same machine.
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The local endpoint is intended for software on your computer to reach Ollama there. Check Ollama’s API and compatibility documentation for the current request format and supported behavior; an endpoint alone does not guarantee that every application or model is compatible.
Ollama also identifies an official Docker image, ollama/ollama. Docker is an alternative deployment path for users who already manage workloads in containers; it is not necessary for the basic native desktop workflow described above.
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What commonly gets in the way of a first run?
Most first-run problems come down to platform support, storage, or choosing a model that is too demanding for the available hardware. Check the specific symptom before changing settings.
The installer or GPU path is unsupported
- On Windows, confirm that the system is Windows 10 22H2 or newer.
- If you expect GPU acceleration, check Ollama’s current platform documentation for the applicable NVIDIA or AMD Radeon driver and runtime requirements.
- Do not assume that a model will use the GPU just because a compatible graphics card is present; inspect the loaded-model status with
ollama ps.
The download fails or the drive fills up
- Check free space on the drive that holds model files, not just the space used by the application.
- Verify the current model-library size and consider moving model storage with
OLLAMA_MODELSif the internal drive is constrained. - Account for every model you keep. Several downloads can add up to tens or hundreds of gigabytes.
The model responds slowly
- Check whether it is running on the CPU, GPU, or both with
ollama ps. - Try a smaller model if the current choice is large relative to the computer’s resources.
- Keep expectations tied to the hardware: Ollama does not publish a single speed that applies across different computers, and its Linux documentation warns that large models can be slow without a strong GPU.
Frequently Asked Questions
What is Ollama’s default context window?
Ollama’s FAQ states a default context window of 2,048 tokens. Context window is a separate setting from the model’s download size; check the current FAQ and model documentation for applicable behavior.
Does Ollama itself contain the AI model?
No. Ollama runs models that you select and download from its library; the runner and the model are distinct pieces of the setup. That is why installing the application does not by itself determine the model’s storage needs or capabilities.
Are the Llama 3.2 sizes listed here current download guarantees?
No. The 1.3GB, 2.0GB, 7.9GB, and 55GB examples come from Ollama’s 2024 documentation snapshot. Model tags and quantizations can change, so use the live library entry for the version you intend to download.
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