Llama 4 Scout vs Maverick: Which to Actually Run Locally

They look like a small/large ladder, but they're built for different machines. Why Scout is the home model, why the smaller one has the bigger… Aliteq Lena Fischer · AI & Local Compute Editor

Scout (109B total, 17B active, 16 experts) is the model you can actually run at home, quantized, on a 32GB GPU or unified memory.

Maverick (400B total, 17B active, 128 experts) is a data-center / heavy-multi-GPU model — not a realistic single-machine home model.

Same active size, very different footprint. Both compute 17B per token, but Maverick's 400B total means ~4× the memory to hold — that's the whole difference for local users.

Scout's edge: the enormous 10M-token context window (vs Maverick's 1M) — ironically the smaller model has the bigger context.

The honest call: run Scout locally; reach for Maverick only via the cloud/API, when you specifically need its extra quality and can't get there with a better-reviewed model.

The smaller model has the bigger context. Scout's context window (up to 10M tokens) dwarfs Maverick's (1M). So if your reason for wanting Llama 4 is long-context work — feeding it whole codebases or…

The counter-intuitive bit

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