Can M3 Max run Llama 3.1 70B Instruct?
Yes — runs locally
~17 tok/sec · Good — slight pause, then text streams smoothly.
The verdict
The M3 Max (128 GB VRAM) handles Llama 3.1 70B Instruct comfortably using the Q5_K_M quantization, which fits in 50.0 GB. Expected throughput is around 17 tokens/second, which feels Good — slight pause, then text streams smoothly. in interactive use. Meta's flagship 70B parameter model. Excellent performance rivaling GPT-4 on many benchmarks.
Setup tutorial: Llama 3.1 70B Instruct on M3 Max
AI-generated, GPU-specific. Verified commands for your exact hardware.
Llama 3.1 70B Instruct runs exceptionally well on the Apple M3 Max with a grade S, using the Q5_K_M quantization. Expect around 23 tokens per second with comfortable performance.
Prerequisites
Before starting, ensure you have at least 100GB of free disk space, macOS Ventura 13.0 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 Q5_K_M quantization, you can expect the model to run at approximately 23 tokens per second, using around 50.0GB of VRAM. Given the 128GB VRAM on the Apple M3 Max, you will have 78.0GB of headroom for context, allowing for a practical context window of up to 131,072 tokens.
1. Install runtimeOllama (preferred on Apple Silicon)
brew install ollama
ollama init2. Download the model
Download the Q5_K_M quantized model, which is 48.0GB in size, from the Hugging Face repository.
ollama pull bartowski/Meta-Llama-3.1-70B-Instruct-GGUF:Meta-Llama-3.1-70B-Instruct-Q5_K_M.gguf3. Run it
ollama run Meta-Llama-3.1-70B-Instruct-Q5_K_M --context-length 131072
ollama chat4. Optimize for M3 Max
To optimize performance on the Apple M3 Max, ensure you are using the Metal/MLX backend to leverage the 128GB of unified memory. This will allow efficient use of both CPU and GPU resources. The Q5_K_M quantization is specifically tuned to balance performance and memory usage, making it ideal for this setup.
Troubleshooting
Insufficient VRAM when running the model
Reduce the context length by adding `--context-length <new_length>` to the `ollama run` command, where `<new_length>` is a lower value.
Slow token generation speed
Ensure that the Metal/MLX backend is enabled. You can check and set this by running `ollama config set backend metal`.
Model fails to load due to file corruption
Re-download the model using the `ollama pull` command provided in the 'downloadModel' section.
Alternative runtimes
While Ollama is the preferred runtime for Apple Silicon, you can also consider alternatives like LM Studio, llama.cpp, or MLX for more advanced customization. Use LM Studio for a graphical interface, llama.cpp for command-line flexibility, and MLX for integrating with other machine learning frameworks. Jan is another option for running models in a web-based environment, but it may not offer the same level of performance as Ollama on the Apple M3 Max.
Other models that run great on M3 Max
FAQ (20)
What GPU do I need to run Llama 3.1 70B Instruct?
To run Llama 3.1 70B Instruct, you need a GPU with at least 40.1 GB of VRAM. Higher VRAM (up to 142.0 GB) is required for full precision or lower quantization levels.
Is Llama 3.1 70B Instruct good for coding?
Yes, Llama 3.1 70B Instruct performs well in coding tasks, often rivaling GPT-4 in code generation and understanding complex programming concepts.
Llama 3.1 70B Instruct vs Llama 3.1 8B?
Llama 3.1 70B Instruct offers significantly better performance and more nuanced responses compared to Llama 3.1 8B, but requires much more VRAM and computational resources.
Can I run Llama 3.1 70B Instruct on a Mac?
Yes, you can run Llama 3.1 70B Instruct on a Mac with a compatible GPU, such as an AMD Radeon Pro or NVIDIA GPU, provided it meets the VRAM requirements.
How much VRAM does Llama 3.1 70B Instruct need?
Llama 3.1 70B Instruct requires between 40.1 GB and 142.0 GB of VRAM, depending on the quantization level used.
Is Llama 3.1 70B Instruct censored?
Llama 3.1 70B Instruct is not inherently censored, but it may have content filters in place to prevent harmful or inappropriate content generation.
Is Llama 3.1 70B Instruct commercial-use allowed?
Yes, Llama 3.1 70B Instruct can be used commercially under the terms of its license, which allows for both research and commercial applications.
Llama 3.1 70B Instruct context length?
Llama 3.1 70B Instruct has a context length of 131,072 tokens, allowing it to process very long sequences of text.
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