text model · mistral · macOS
Can I run Mistral 7B on Apple M4 (16GB)?
Yes. Mistral 7B runs on Apple M4 (16GB) at Q4_K_M (~5.8 GB of ~10.5 GB usable).
Runs at Q4_K_M using ~5.8 GB of ~10.5 GB usable. You have room for Q8_0 for higher quality.
- Q4_K_M needed
- ~5.8 GB
- Usable on device
- ~10.5 GB
- Device memory
- 16 GB
- Best quant
- Q4_K_M
Run it
Pick your tool. All three load the same Q4_K_M weights.
ollama run mistral:7b llama-cli -hf bartowski/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_M lms get bartowski/Mistral-7B-Instruct-v0.3-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
- 7B
- Q4_K_M size
- 4.37 GB
- Q8_0 size
- 7.7 GB
- Context
- 32k
- Ollama tag
- mistral:7b
- Memory
- 16 GB unified
- Usable for weights
- ~10.5 GB
- Best runtime
- Ollama (MLX backend, preview) / MLX direct
You could also run
Run Mistral 7B on other hardware
FAQ
Can Apple M4 (16GB) run Mistral 7B?
Yes. Mistral 7B runs on Apple M4 (16GB) at Q4_K_M (~5.8 GB of ~10.5 GB usable).
How much memory does Mistral 7B need?
Apple M4 (16GB) has room to spare. At Q4_K_M the weights are ~4.37 GB; with KV cache and runtime overhead, budget ~5.8 GB at a 4k context.
What is the best tool to run Mistral 7B 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.