text model · mistral · macOS
Can I run Mixtral 8x7B on Apple M3 Pro (18GB)?
No. Mixtral 8x7B needs ~28.9 GB even at Q4_K_M, but Apple M3 Pro (18GB) only has ~12 GB usable.
Needs ~28.9 GB even at Q4_K_M, but only ~12 GB is usable.
- Q4_K_M needed
- ~28.9 GB
- Usable on device
- ~12 GB
- Device memory
- 18 GB
- 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
- 18 GB unified
- Usable for weights
- ~12 GB
- Best runtime
- Ollama (llama.cpp Metal backend) / MLX
What you can run instead
Run Mixtral 8x7B on other hardware
FAQ
Can Apple M3 Pro (18GB) run Mixtral 8x7B?
No. Mixtral 8x7B needs ~28.9 GB even at Q4_K_M, but Apple M3 Pro (18GB) only has ~12 GB usable.
How much memory does Mixtral 8x7B need?
Apple M3 Pro (18GB) does not have enough memory. 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.