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For a documented Linux install, Ollama provides a short setup command and a command to download and run Llama 3.2. Ubuntu also documents an Ollama Snap installation. The commands below come from those projects’ documentation; they are not independent tests on Ubuntu 26.04 or 24.04, and neither source provides a compatibility matrix for the two releases.
What you need to know before installing
Ubuntu’s AI-on-Ubuntu page says a fresh Ubuntu 26.04 LTS Desktop installation includes no built-in AI tooling. Installing a runtime such as Ollama is therefore a separate step. The Ubuntu documentation index lists both Ubuntu 26.04 LTS and 24.04 LTS, but the sources cited here do not certify every installation route on each release.
A model’s parameter count, listed download size, installed storage use, RAM requirement, GPU VRAM requirement, and speed are different things. Treat model sizes below as Ollama’s listed download figures, not as a promise that a model will fit or run quickly on a particular computer.
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Install and run Llama with Ollama
Linux install script
Ollama documents this command for Linux installation:
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curl -fsSL https://ollama.com/install.sh | shAfter installation, run the Llama 3.2 example:
ollama run llama3.2Ollama says this command downloads the model if needed and opens an interactive chat. These are documented commands, not commands independently tested on Ubuntu 26.04 or 24.04. See the Ollama quickstart.
Ubuntu Snap route
Ubuntu’s AI-on-Ubuntu page documents installing Ollama as a Snap:
sudo snap install ollamaThat page’s sample run command uses Qwen, not Llama. To use Llama after installing the Snap, run Ollama’s documented Llama command:
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Choose a Llama model and understand its listed size
Ollama’s quickstart lists the following model examples and commands. The GB figures are the page’s listed download sizes; library tags and sizes can change, so consult the Ollama quickstart if exact current values matter.
| Model example | Parameters | Listed download size | Run command |
|---|---|---|---|
| Llama 3.2 | 3B | 2.0 GB | ollama run llama3.2 |
| Llama 3.2 | 1B | 1.3 GB | ollama run llama3.2:1b |
| Llama 3.2 Vision | 11B | 7.9 GB | ollama run llama3.2-vision |
| Llama 3.2 Vision | 90B | 55 GB | ollama run llama3.2-vision:90b |
| Llama 3.1 | 8B | 4.7 GB | ollama run llama3.1 |
| Llama 3.1 | 70B | 40 GB | ollama run llama3.1:70b |
| Llama 3.1 | 405B | 231 GB | ollama run llama3.1:405b |
For a first run, smaller parameter counts are the more plausible place to start if your machine has limited resources, but the table does not establish a minimum RAM figure for each listed variant. The download size is not total disk use after installation, nor is it a measure of RAM or GPU VRAM needed while running.
How much RAM does Llama need?
Ollama’s quickstart gives broad available-RAM guidance by parameter count: at least 8 GB for 7B models, 16 GB for 13B models, and 32 GB for 33B models. This is general guidance from Ollama, not a fit guarantee or a GPU requirement. It does not specify context length or promise a particular speed, and it should not be extrapolated as an exact minimum for every Llama variant above.
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If considering a 32GB RAM kit for a computer intended to run larger models, first check the device’s supported memory type, maximum capacity, and upgradeability. The documentation does not establish a universal hardware configuration.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Useful Ollama commands after installation
Ollama documents these commands for managing a local model:
ollama pull llama3.2downloads or updates the model; Ollama says an update downloads only the difference.ollama listlists locally available models.ollama show llama3.2displays model information.ollama psshows running models.ollama stop llama3.2stops the model.ollama rm llama3.2removes the model.
Ollama also documents ollama serve to start Ollama without its desktop application. Its local REST API is documented at http://localhost:11434, with /api/generate and /api/chat endpoints. These are documentation details, not evidence of a security audit or a privacy guarantee.
When to use llama.cpp and GGUF instead
llama.cpp is an alternative if you specifically want its CLI or a GGUF-based workflow. Its model documentation says compatible models can be fetched with -hf <user>/<model>[:quant] or run from a local file. Models must be in GGUF format; other formats can be converted with project scripts. Its documented example uses Gemma, not Llama:
llama cli -hf ggml-org/gemma-3-1b-it-GGUFThat example should not be mistaken for a Llama command. No specific current Llama GGUF repository and tag, or verified end-to-end Llama command, is established here. GGUF download sizes also depend on the model and quantization, so Ollama’s listed sizes above do not apply. See the llama.cpp model documentation.
Ubuntu version and hardware caveats
The Ubuntu AI page was last edited on September 25, 2026. It states: “As of Ubuntu 26.04 LTS, a fresh installation of Ubuntu Desktop contains no built-in AI tooling.” The reviewed documentation does not provide an Ollama compatibility matrix or separate test results for Ubuntu 26.04 and 24.04. Hardware acceleration can also depend on the specific machine and drivers; these sources do not support a GPU recommendation or a conclusion about acceleration on either release.
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