Can M4 Max run Mixtral 8x7B Instruct?
Yes — runs locally
~26 tok/sec · Good — slight pause, then text streams smoothly.
The verdict
The M4 Max (128 GB VRAM) handles Mixtral 8x7B Instruct comfortably using the Q5_K_M quantization, which fits in 30.5 GB. Expected throughput is around 26 tokens/second, which feels Good — slight pause, then text streams smoothly. in interactive use. The OG public MoE — 8 experts, 2 active per token, 47 B total / 13 B active. Apache-2.0.
Setup tutorial: Mixtral 8x7B Instruct on M4 Max
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Mixtral 8x7B Instruct on an Apple M4 Max with Grade S performance, using the Q5_K_M quantization for ~44 tok/sec.
Prerequisites
Before starting, ensure you have at least 150GB of free disk space, macOS 13.0 or later, and Xcode Command Line Tools installed. You can install Xcode CLT by running `xcode-select --install`.
Expected performance
With the Q5_K_M quantization, you can expect ~44 tok/sec performance, utilizing 30.5GB of VRAM. Given the remaining 97.5GB of VRAM, you can achieve a practical context window of up to 32768 tokens, making it suitable for long-form content generation.
1. Install runtimeOllama (preferred on Apple Silicon)
brew install ollama
ollama init2. Download the model
Download the Q5_K_M quantized model (30.0GB file) from Hugging Face.
ollama pull TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF:mixtral-8x7b-instruct-v0.1.Q5_K_M.gguf3. Run it
ollama run mixtral-8x7b-instruct-v0.1.Q5_K_M.gguf --interactive
ollama chat mixtral-8x7b-instruct-v0.1.Q5_K_M.gguf4. Optimize for M4 Max
To optimize performance on the Apple M4 Max, ensure you are using the Metal/MLX backend. The 128GB of unified memory allows for efficient use of both CPU and GPU resources. With 30.5GB VRAM in use, you have 97.5GB of headroom for large context windows and additional tasks.
Troubleshooting
Out of memory errors during inference
Reduce the batch size or context length to fit within the 128GB VRAM limit.
Slow inference speed
Ensure you are using the Metal/MLX backend and that all necessary drivers are up to date.
Model fails to load
Verify that the model file is correctly downloaded and not corrupted. Try re-downloading the model using the `ollama pull` command.
Alternative runtimes
Alternative runtimes include LM Studio, llama.cpp, and MLX. LM Studio offers a graphical interface and is useful for users who prefer a visual setup. llama.cpp is more lightweight and can be used for resource-constrained environments. MLX is another option for Apple Silicon, providing a balance between performance and ease of use. Choose based on your specific needs and comfort level with command-line tools.
Other models that run great on M4 Max
FAQ (20)
What GPU do I need to run Mixtral 8x7B Instruct?
To run Mixtral 8x7B Instruct, you need a GPU with at least 25.1 GB of VRAM, but 30.5 GB is recommended for optimal performance.
Is Mixtral 8x7B Instruct good for coding?
Mixtral 8x7B Instruct is well-suited for coding tasks due to its large context length of 32,768 tokens and strong language understanding capabilities.
Mixtral 8x7B Instruct vs Llama 3.1 8B?
Mixtral 8x7B Instruct has more parameters (46.7B vs 8B) and a longer context length (32,768 vs 2,048), making it more powerful for complex tasks but requiring more VRAM.
Can I run Mixtral 8x7B Instruct on a Mac?
Yes, you can run Mixtral 8x7B Instruct on a Mac, but you will need a Mac with an M1 or later chip and sufficient VRAM to handle the model's requirements.
How much VRAM does Mixtral 8x7B Instruct need?
Mixtral 8x7B Instruct requires between 25.1 GB and 30.5 GB of VRAM, depending on the quantization level used.
Is Mixtral 8x7B Instruct censored?
No, Mixtral 8x7B Instruct is not censored; it provides uncensored responses based on the input it receives.
Is Mixtral 8x7B Instruct commercial-use allowed?
Yes, Mixtral 8x7B Instruct is licensed under the Apache-2.0 license, which allows for commercial use.
Mixtral 8x7B Instruct context length?
The context length of Mixtral 8x7B Instruct is 32,768 tokens, allowing it to handle very long inputs and maintain context over extended conversations.
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