Run Llama 4 Scout on Unified Memory (Strix Halo & Mac Studio)

A quality Q4 Scout wants ~62GB — which no single consumer GPU has, but a 96–128GB Strix Halo or Mac Studio holds whole. Why unified memory is… Aliteq Ravi Malhotra · Hardware Editor

Unified memory holds what GPUs can't. 96–128GB of shared CPU/GPU memory fits a full Q4 Scout (~62GB) plus a big KV cache — the whole MoE, in one box.

It's often the cheaper path to capacity. A 128GB Strix Halo mini-PC or a Mac Studio can cost less than stacking enough GPU VRAM to match, and it sips power.

The trade-off is bandwidth, not fit. Unified memory is slower than a discrete GPU's VRAM, so you get fewer tokens/second — you're trading peak speed for the ability to run the model at good quality…

MoE softens the speed hit. Because only 17B parameters are active per token, Scout runs faster on unified memory than a dense 109B would — the architecture and the hardware suit each other.

Two main options: AMD Strix Halo (Ryzen AI Max+) mini-PCs and Apple's Mac Studio. Both are covered in depth in our hardware cluster.

Unified memory has less bandwidth than a high-end GPU's VRAM, so your token generation will be slower than the same model would run on (a big enough) GPU. For interactive chat and long-context work…

The honest trade-off: tokens per second

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