text model · Llama 4 · macOS
Can I run Llama 4 Scout on Apple M5 Max (128GB)?
Yes. Llama 4 Scout runs on Apple M5 Max (128GB) at Q4_K_M (~64.2 GB of ~96 GB usable).
Runs at Q4_K_M using ~64.2 GB of ~96 GB usable.
- Q4_K_M needed
- ~64.2 GB
- Usable on device
- ~96 GB
- Device memory
- 128 GB
- Best quant
- Q4_K_M
Run it
Pick your tool. All three load the same Q4_K_M weights.
ollama run llama4:scout llama-cli -hf unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF:Q4_K_M lms get unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF 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.
- Parameters
- 109B (MoE, 17B active)
- Q4_K_M size
- 60.87 GB
- Context
- 128k
- Ollama tag
- llama4:scout
- Memory
- 128 GB unified
- Usable for weights
- ~96 GB
- Best runtime
- MLX direct / Ollama (MLX backend)
You could also run
Run Llama 4 Scout on other hardware
FAQ
Can Apple M5 Max (128GB) run Llama 4 Scout?
Yes. Llama 4 Scout runs on Apple M5 Max (128GB) at Q4_K_M (~64.2 GB of ~96 GB usable).
How much memory does Llama 4 Scout need?
Apple M5 Max (128GB) has room to spare. 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.
Sources
Memory figures are estimates. See methodology.