Can M4 Pro run Mixtral 8x7B Instruct?
Yes — runs locally
~17 tok/sec · Good — slight pause, then text streams smoothly.
The verdict
The M4 Pro (48 GB VRAM) handles Mixtral 8x7B Instruct comfortably using the Q4_K_M quantization, which fits in 25.1 GB. Expected throughput is around 17 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 Pro
AI-generated, GPU-specific. Verified commands for your exact hardware.
The Mixtral 8x7B Instruct model runs Grade A on an Apple M4 Pro with Q4_K_M quantization, achieving ~20 tok/sec and using 25.1GB VRAM.
Prerequisites
Before starting, ensure you have at least 50GB of free disk space, macOS 12.3 or later, and Xcode Command Line Tools installed. You can install Xcode CLT by running `xcode-select --install` in your terminal.
Expected performance
With the Q4_K_M quantization, you can expect the model to run at approximately 20 tokens per second, using 25.1GB of VRAM. Given the 48GB VRAM on the Apple M4 Pro, you will have 22.9GB of headroom for context, enabling a practical context window of up to 32768 tokens.
1. Install runtimeOllama (preferred on Apple Silicon)
brew install ollama
ollama init2. Download the model
Download the Q4_K_M quantized version of the Mixtral 8x7B Instruct model (24.6GB file size).
ollama pull TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF:mixtral-8x7b-instruct-v0.1.Q4_K_M.gguf3. Run it
ollama run TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF:mixtral-8x7b-instruct-v0.1.Q4_K_M.gguf
ollama chat4. Optimize for M4 Pro
To optimize performance on the Apple M4 Pro, leverage the Metal/MLX backend and utilize the 48GB of unified memory. Ensure that the MPS layers are enabled to take full advantage of the GPU. With 25.1GB VRAM in use, you will have 22.9GB of headroom for context, allowing for a practical context window of up to 32768 tokens.
Troubleshooting
If you encounter an out-of-memory error during inference, try reducing the batch size or context length.
ollama config set batch_size 1
If the model runs but is significantly slower than expected, check if the Metal/MLX backend is properly configured.
ollama config set backend metal
If you see errors related to MPS layers, ensure they are enabled.
ollama config set mps true
Alternative runtimes
For users who prefer different runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for fine-grained control over quantization, or MLX for direct Metal integration. Jan is another option for those who need a lightweight runtime. However, Ollama is generally recommended for its ease of use and performance on Apple Silicon.
Other models that run great on M4 Pro
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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