Can I run Llama 4 Scout?

Llama 4 Scout is Meta's mixture-of-experts model, and the key thing to understand is the gap between its two sizes. It has 109B total parameters but only routes 17B of them per token, so it generates at the speed of a 17B model while still demanding the memory of the full 109B. At a 4-bit quant that is about 66 GB on disk, and you need at least 96 GB of RAM or unified memory to load it. This is not a 24 GB consumer GPU model: both an RTX 3060 and an RTX 4090 simply do not fit it. Realistically this is a workstation or a maxed-out Apple Silicon machine.

On an Apple M Max with enough unified memory it runs around 34 tokens per second at 4-bit, which is comfortably faster than you read and fine for interactive chat and its vision tasks. On CPU with DDR5 you drop to roughly 5 tokens per second, usable for batch work but painful for back-and-forth. The advertised context is enormous, but be careful: pushing toward 128K tokens drives total memory to about 120 GB, so the long context is a hardware-budget decision, not a free setting. Keep working context modest unless you have headroom to spare.

Against GPT-OSS 120B, the other large model in this class, Scout is the multimodal pick since it handles vision, while GPT-OSS generally has the edge on pure reasoning. Compared to small siblings like Llama 3.2 3B, Scout is a completely different commitment: those run on a phone-class footprint, this needs a server. Its standout trait is being a genuinely fast, image-capable model at this scale. One caveat: it ships under the Llama Community license, which is open-weight rather than open-source, so check the terms before building anything commercial on it.