text model · Llama 4 · macOS
Can I run Llama 4 Scout on Apple M5 Pro (48GB)?
No. Llama 4 Scout needs ~64.2 GB even at Q4_K_M, but Apple M5 Pro (48GB) only has ~32 GB usable.
Needs ~64.2 GB even at Q4_K_M, but only ~32 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 32.2 GB: Llama 4 Scout needs roughly 64.2 GB at Q4_K_M and Apple M5 Pro (48GB) leaves only about 32 GB usable for a model. The lightest tracked hardware that runs Llama 4 Scout is the Apple M4 Max (128GB) at 128 GB. See Llama 4 Scout on Apple M4 Max (128GB).
- Q4_K_M needed
- ~64.2 GB
- Usable on device
- ~32 GB
- Device memory
- 48 GB
Which quant fits
- Parameters
- 109B (MoE, 17B active)
- Q4_K_M size
- 60.87 GB
- Q8_0 size
- 106.67 GB
- Context
- 128k
- Ollama tag
- llama4:scout
- Memory
- 48 GB unified
- Usable for weights
- ~32 GB
- Best runtime
- MLX direct / Ollama (MLX backend)
What you can run instead
Run Llama 4 Scout on other hardware
FAQ
Can Apple M5 Pro (48GB) run Llama 4 Scout?
No. Llama 4 Scout needs ~64.2 GB even at Q4_K_M, but Apple M5 Pro (48GB) only has ~32 GB usable.
How much memory does Llama 4 Scout need?
Apple M5 Pro (48GB) does not have enough memory. At Q4_K_M the weights are ~60.87 GB; with KV cache and runtime overhead, budget ~64.2 GB at a 4k context. It is a Mixture-of-Experts model (109B total / 17B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run Llama 4 Scout on macOS?
LM Studio for a simple setup; mlx-lm for the most speed. vLLM is NOT a Mac tool, it is a CUDA/Linux serving engine. Unified memory is not a fixed VRAM slice; ~70% is usable for weights.
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A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/llama-4-scout/apple-m5-pro-48gb) Sources
Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.