How to Run Llama 4 Locally in 2026 (The Honest Hardware Guide)

Llama 4 didn't dethrone Qwen3 or DeepSeek — but its MoE design and huge context make it worth understanding. What Scout and Maverick actually are,… Aliteq Lena Fischer · AI & Local Compute Editor

Two models, one active size. Llama 4 Scout is 109B total parameters with 17B active (16 experts); Maverick is 400B total with 17B active (128 experts). Both are mixture-of-experts and natively…

The MoE trap for local users: "17B active" sounds light, but MoE loads ALL the parameters into memory — Scout needs the room for 109B, not 17B. This is the single most misunderstood thing about…

Real local footprint (Scout): ~62GB at Q4_K_M (so a 64GB+ card or unified memory), or ~33GB with aggressive ~1.78-bit dynamic quant — which fits a 32GB card, or a 24GB card with CPU expert-offload…

Be honest about quality: on coding/reasoning, Qwen3 and DeepSeek generally beat Scout at similar effort. Llama 4's edges are its 10M-token context and native multimodal input.

Where it shines locally: big unified-memory machines (Strix Halo, Mac Studio) that can hold the whole MoE — that fit is the actual reason to care, and where this cluster points.

Cheap compute per token does NOT mean small memory. Because the router might pick any expert at any moment, all of the parameters have to be resident in memory. Scout is a 109B model for the…

The catch that trips up every local build

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