Run Llama 4 Scout locally: hardware requirements
Llama 4 Scout by Meta installs on a paired computer through idapt's local engine. RAM decides whether a build fits; a GPU only changes speed. Local runs are not billed.
- Download
- 63 GB
- RAM needed
- 84 GB
- Builds
- 1
Will it run on your machine?
Enter your system RAM to get verdicts. RAM decides whether a build fits; a GPU only changes speed.
Every build
| Build | Quantization | Download | RAM needed |
|---|---|---|---|
| 16x17bDefault | full precision | 63 GB | 84 GB |
RAM figures are working-set recommendations: when GPU memory is short, the runtime spills to CPU and system memory instead of failing.
Install it
Pair a computer with idapt and install Llama 4 Scout from the model page: the local engine bootstraps itself, pulls the build you pick, and streams progress. Or pull it directly:
ollama pull llama4:16x17bFrequently asked
How much RAM do I need to run Llama 4 Scout locally?
The smallest Llama 4 Scout build runs with about 84 GB of system RAM; the largest needs 84 GB. RAM is the binding requirement: when GPU memory is short, the runtime spills to CPU and system memory instead of failing.
Which quantization of Llama 4 Scout should I pick?
Start with the default 16x17b build: a 63 GB download that wants about 84 GB of RAM. Smaller quantizations shrink the download and memory needs at some cost in output quality; larger ones do the reverse.
Do I need a GPU to run Llama 4 Scout?
No. A GPU with enough VRAM makes generation faster, but Llama 4 Scout runs on CPU and system RAM alone. Whether a build fits is decided by RAM; the GPU only changes speed.
How big is the Llama 4 Scout download?
About 63 GB for the single available build.
Can idapt install Llama 4 Scout for me?
Yes. Pair a computer with idapt and install Llama 4 Scout from this page: the local engine bootstraps itself, pulls the build you pick (ollama pull llama4), and streams download progress. Local runs are not billed.
Part of the Llama 4 Scout model page · Llama 4 Scout pricing · Check every local model against your hardware