How to Run Llama 4 Locally on Your Own Machine

Llama 4 is Meta’s open-weight language model family, released in April 2025. The headline is a mixture-of-experts design: the Scout variant has 109 billion total parameters but activates only about 17 billion at a time, which is why it can run on hardware that would choke on a dense model that size.

You download the weights, run them on your own machine, and pay no per-token fee. The trade is setup effort and hardware.

What Do You Need to Run Llama 4?

Scout runs with quantisation on a single GPU with 24GB of VRAM, or on a 32GB+ Apple Silicon Mac. The larger Maverick variant needs substantially more — think multiple GPUs or a beefy cloud instance. Run the numbers before you commit; a rented GPU often beats buying one for occasional use.

Variant	Total params	Active params	Realistic hardware
Scout	109B	~17B	1x 24GB GPU or 32GB Mac
Maverick	400B+	~17B	Multi-GPU

How Do You Actually Run It?

1. Install a runtime: Ollama or llama.cpp are the easy paths.
2. Pull a quantised build of the model (Ollama handles this with one command).
3. Point your chat client or API wrapper at the local endpoint.
4. Test a real prompt and measure speed — tokens per second tells you if it is usable.

Start with a 4-bit quantised version. Full precision rarely justifies the extra memory for everyday work.

Llama 4 vs a Hosted API: Which Should You Use?

Run it locally for privacy, offline use, or high-volume jobs where per-token fees add up. Use a hosted API when you want the strongest model, no setup and someone else handling updates. For most people, a mid-tier hosted model runs rings around a heavily quantised local Llama 4 on hard tasks.

Key Takeaways

• Llama 4 (April 2025) is open-weight with a MoE design; Scout activates ~17B of 109B params.
• Scout runs on one 24GB GPU or a 32GB Mac with quantisation.
• Start with a 4-bit quantised build via Ollama or llama.cpp.
• Choose local for privacy and volume; hosted for top quality and zero setup.