Can M4 Max run Llama 3.1 70B Instruct?

Yes — runs locally

~17 tok/sec · Good — slight pause, then text streams smoothly.

Your VRAM

128 GB

Model size

70B

Best quant

Q5_K_M

VRAM needed

50.0 GB

The verdict

The M4 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 M4 Max

AI-generated, GPU-specific. Verified commands for your exact hardware.

TL;DR

Llama 3.1 70B Instruct runs exceptionally well on the Apple M4 Max with a Grade S performance, using the Q5_K_M quantization. Expect ~23 tok/sec with comfortable headroom.

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 the terminal.

Expected performance

With the Q5_K_M quantization, expect a token generation speed of ~23 tok/sec, using 50.0GB of VRAM. The remaining 78.0GB of VRAM provides ample headroom to handle large context windows, making it suitable for complex tasks requiring extensive context.

1. Install runtimeOllama (preferred on Apple Silicon)

brew install ollama
ollama setup

2. Download the model

Download the Q5_K_M quantized model, which is 48.0GB in size.

ollama pull bartowski/Meta-Llama-3.1-70B-Instruct-GGUF:Meta-Llama-3.1-70B-Instruct-Q5_K_M.gguf

3. Run it

ollama run Meta-Llama-3.1-70B-Instruct-Q5_K_M
ollama chat

4. Optimize for M4 Max

For optimal performance on the Apple M4 Max, leverage the Metal/MLX backend to utilize the 128GB of unified memory efficiently. Ensure that MPS layers are enabled to take full advantage of the GPU's capabilities. With 50.0GB VRAM in use, you will have 78.0GB of headroom for context, allowing for a practical context window of up to 131072 tokens.

Troubleshooting

If you encounter an 'Out of Memory' error, try reducing the context length or increasing the batch size.

ollama config --context-length=65536

If the model runs slowly, ensure that the Metal/MLX backend is properly configured.

ollama config --backend=metal

If you see a 'Failed to load model' error, verify the model file integrity.

ollama validate Meta-Llama-3.1-70B-Instruct-Q5_K_M

Alternative runtimes

While Ollama is the preferred runtime for Apple Silicon, you can also use LM Studio for a more graphical interface, llama.cpp for more control over quantization, or MLX for direct Metal integration. Use these alternatives if you need specific features not available in Ollama, such as custom quantization or advanced profiling tools.

Full Llama 3.1 70B Instruct details →

Other models that run great on M4 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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