Can M4 Pro run Llama 3.1 70B Instruct?

Yes — runs locally

~0 tok/sec · Cannot run — model too large for this GPU

Your VRAM

48 GB

Model size

70B

Best quant

Q4_K_M

VRAM needed

40.1 GB

The verdict

The M4 Pro (48 GB VRAM) handles Llama 3.1 70B Instruct comfortably using the Q4_K_M quantization, which fits in 40.1 GB. Expected throughput is around 0 tokens/second, which feels Cannot run — model too large for this GPU 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 Pro

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

TL;DR

Llama 3.1 70B Instruct runs on Apple M4 Pro with a grade C, using the Q4_K_M quantization. Expect ~11 tokens per second with 40.1GB VRAM usage.

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 ~11 tokens per second and 40.1GB of VRAM in use, leaving approximately 7.9GB of headroom for context. This allows for a practical context window of around 65,536 tokens, given the remaining VRAM.

1. Install runtimeOllama (preferred on Apple Silicon)

brew install ollama
ollama init

2. Download the model

Download the Q4_K_M quantized model, which is 39.6GB in size.

ollama pull bartowski/Meta-Llama-3.1-70B-Instruct-GGUF:Meta-Llama-3.1-70B-Instruct-Q4_K_M.gguf

3. Run it

ollama run Meta-Llama-3.1-70B-Instruct-Q4_K_M
ollama chat

4. Optimize for M4 Pro

To optimize performance on the Apple M4 Pro, ensure you are using the Metal/MLX backend to leverage the 48GB of unified memory. The Q4_K_M quantization is well-suited for this GPU, minimizing VRAM usage while maintaining reasonable performance. Enable MPS layers to further enhance efficiency.

Troubleshooting

Low token generation speed

Ensure that the Metal/MLX backend is enabled and that MPS layers are utilized. You can check this by running `ollama config show` and verifying the settings.

Out of memory errors

Reduce the context length to fit within the available VRAM. For example, set the context length to 65,536 tokens by running `ollama config set context_length 65536`.

Model not found

Verify that the model was successfully downloaded by running `ollama list`. If not, re-run the download command.

Alternative runtimes

For more advanced users, alternatives like LM Studio, llama.cpp, and MLX can offer additional customization options. LM Studio provides a graphical interface, while llama.cpp is highly optimized for Apple Silicon. MLX is another runtime that leverages Metal for efficient execution. Use these alternatives if you need more control over the model's execution or if you encounter issues with Ollama.

Full Llama 3.1 70B Instruct details →

Other models that run great on M4 Pro

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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