About Llama 4 Scout
Llama 4 Scout is Meta's 109B-parameter model released in April 2025, with a 10240K-token context window. It is a mixture-of-experts model: all 109B parameters must sit in memory, but only ~17B are active per token, which is what makes it fast for its size. It uses a classic dense-attention design whose KV cache grows linearly with context: its KV cache is about 1.6 GB at an 8K context, 25.8 GB at 128K, and 2061.6 GB at the full 10240K window (FP16 cache).
For most people Q4_K_M is the sweet spot — the most popular quality/size trade-off — while Q8 is near-lossless if you have the memory. Totals above include the KV cache and a realistic framework overhead, so they are what you should expect to see in practice rather than just the download size. Weight sizes are calibrated against real GGUF files — see themethodology.
Frequently asked questions
How much VRAM does Llama 4 Scout need?
At Q4_K_M with an 8K context, Llama 4 Scout needs about 76 GB (weights 66 GB + KV cache + overhead). The smallest common hardware that fits is a A100 80GB.
Can an RTX 4090 (24GB) run Llama 4 Scout?
Not fully in VRAM. Llama 4 Scout needs about 76 GB even at Q4_K_M, so a 24 GB card would have to offload layers to system RAM at a large speed penalty.
Can a Mac run Llama 4 Scout?
Yes — Apple Silicon with 128 GB of unified memory or more (macOS lets the GPU use ~75% of it, ~96 GB) runs Llama 4 Scout at Q4_K_M.
Related
VRAM calculator for any model ·Token counter
Last updated 2026-08-03. Architecture figures from the model's published config.json; see themethodology.