text model · mistral · macOS
Can I run Mixtral 8x7B on Apple M5 Pro (48GB)?
Yes. Mixtral 8x7B runs on Apple M5 Pro (48GB) at Q4_K_M (~28.9 GB of ~32 GB usable).
Fits at Q4_K_M (~28.9 GB of ~32 GB usable) but with little headroom, close other apps.
- Q4_K_M needed
- ~28.9 GB
- Usable on device
- ~32 GB
- Device memory
- 48 GB
- Best quant
- Q4_K_M
Run it
Pick your tool. All three load the same Q4_K_M weights.
ollama run mixtral:8x7b llama-cli -hf MaziyarPanahi/Mixtral-8x7B-Instruct-v0.1-GGUF:Q4_K_M lms get MaziyarPanahi/Mixtral-8x7B-Instruct-v0.1-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
- 46.7B (MoE, 12.9B active)
- Q4_K_M size
- 26.49 GB
- Q8_0 size
- 46.22 GB
- Context
- 32k
- Ollama tag
- mixtral:8x7b
- Memory
- 48 GB unified
- Usable for weights
- ~32 GB
- Best runtime
- MLX direct / Ollama (MLX backend)
You could also run
Run Mixtral 8x7B on other hardware
FAQ
Can Apple M5 Pro (48GB) run Mixtral 8x7B?
Yes. Mixtral 8x7B runs on Apple M5 Pro (48GB) at Q4_K_M (~28.9 GB of ~32 GB usable).
How much memory does Mixtral 8x7B need?
It is a tight fit on Apple M5 Pro (48GB). At Q4_K_M the weights are ~26.49 GB; with KV cache and runtime overhead, budget ~28.9 GB at a 4k context. It is a Mixture-of-Experts model (46.7B total / 12.9B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run Mixtral 8x7B 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.