text model · Mistral · macOS
Can I run Mixtral 8x7B on Apple M5 (16GB)?
No. Mixtral 8x7B needs ~28.9 GB even at Q4_K_M, but Apple M5 (16GB) only has ~10.5 GB usable.
Needs ~28.9 GB even at Q4_K_M, but only ~10.5 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 18.4 GB: Mixtral 8x7B needs roughly 28.9 GB at Q4_K_M and Apple M5 (16GB) leaves only about 10.5 GB usable for a model. The lightest tracked hardware that runs Mixtral 8x7B is the Nvidia GeForce RTX 5090 (32GB) at 32 GB. See Mixtral 8x7B on Nvidia GeForce RTX 5090 (32GB).
- Q4_K_M needed
- ~28.9 GB
- Usable on device
- ~10.5 GB
- Device memory
- 16 GB
Which quant fits
- 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
- 16 GB unified
- Usable for weights
- ~10.5 GB
- Best runtime
- MLX direct / Ollama (MLX backend)
What you can run instead
Run Mixtral 8x7B on other hardware
FAQ
Can Apple M5 (16GB) run Mixtral 8x7B?
No. Mixtral 8x7B needs ~28.9 GB even at Q4_K_M, but Apple M5 (16GB) only has ~10.5 GB usable.
How much memory does Mixtral 8x7B need?
Apple M5 (16GB) 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.
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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.