Llama is the open model that kicked off the whole local-AI movement, and running it yourself is genuinely one command. Here's how, and which size fits your GPU.
One command: install Ollama, then run `ollama run llama3.3` — it downloads the model and starts a chat. Llama, Meta's open model family, is the one that kicked off the entire local-AI movement, and it's still one of the most popular and best-supported models you can run at home. It comes in an efficient 8B and a powerful 70B, so there's a version for modest cards and for serious rigs, and it runs privately and offline for free. Here's the full guide, including which size to pick and how Llama stacks up against Qwen3 and Mistral.
Install and pick your size
The fastest way in is Ollama. Install it, then run the size that fits your GPU:
# install Ollama from ollama.com, then run the size that fits your GPU:
ollama run llama3.1:8b # Llama 8B — ~8GB VRAM, fast and capable
ollama run llama3.3 # Llama 70B — ~48GB (multi-GPU or big unified memory)
# first run downloads the model, then you're chatting. Or use LM Studio for a GUI.
Match it to your [VRAM](/how-much-vram-do-you-need-to-run-ai-models-2026): Llama 8B runs great on an 8GB card and is the version most people use; Llama 70B is far more capable but needs ~48GB, so two 24GB GPUs or a big-memory Mac. For the 70B specifically, see our best GPU for Llama 70B guide.
Why Llama, and how it compares
Llama's superpower isn't that it's always the single best model — it's that it's the most supported and battle-tested. Because Llama started the open-model movement, nearly every tool, tutorial, fine-tune, and framework targets it first, so if you're following a guide or wiring up agents, Llama is the safe default that just works. It's also strongly trained for tool use and function-calling, which makes it excellent for agentic setups. On raw quality, the picture in 2026 is competitive: Qwen3 often edges ahead at a given size, Gemma 3 adds image understanding, and Mistral wins on efficiency — but Llama's ecosystem, tool-calling strength, and the huge library of community fine-tunes built on it (for coding, roleplay, and more) keep it a top pick. It's one of the best free local models, and a great first model to run.
Llama is the model that started local AI — the most supported, best for tool-use and community fine-tunes. · Unsplash
Quick answers
How do I run Llama locally?
Install Ollama (free, from ollama.com), then run 'ollama run llama3.1:8b' for the 8B model or 'ollama run llama3.3' for the 70B. The first run downloads the model and starts a chat; it runs privately and offline on your hardware, free. If you prefer a graphical app, LM Studio runs the same Llama models. Match the size to your GPU: Llama 8B needs ~8GB of VRAM (fits most cards), while Llama 70B needs ~48GB, meaning two 24GB GPUs or a large-memory Mac.
Which Llama size should I run?
For most people, Llama 8B — it runs on an 8GB GPU, is fast, and is genuinely capable for chat, coding help, and agents. Step up to Llama 70B only if you have the hardware (~48GB of VRAM, so two 24GB GPUs or a big-memory Mac) and want maximum quality. The 8B is the version the vast majority of local-AI users run, and it covers most everyday tasks well. If you're unsure, start with 8B and only move to 70B if you hit its limits.
Is Llama the best local AI model?
Llama isn't always the single highest-quality model, but it's the most supported and best-established, which makes it a top pick. Because it launched the open-model movement, nearly every tool, tutorial, and fine-tune targets Llama first, and it's strongly trained for tool use, making it ideal for agents. In 2026, Qwen3 often edges it on raw quality, Gemma 3 adds image support, and Mistral wins on efficiency — but Llama's ecosystem, tool-calling, and vast library of community fine-tunes keep it excellent and a great default choice.
Llama is the model that started it all — one command to run, the best-supported, and ideal for agents and fine-tunes. Compare with Qwen3 and Mistral, see the best free models, and size it in the VRAM calculator. Source: Meta and Ollama.
