Can M4 Pro run Llama 3.1 8B Instruct?

Yes — runs locally

~38 tok/sec · Fast — smooth conversation. Responses feel real-time.

Your VRAM

48 GB

Model size

Best quant

FP16

VRAM needed

17.0 GB

The verdict

The M4 Pro (48 GB VRAM) handles Llama 3.1 8B Instruct comfortably using the FP16 quantization, which fits in 17.0 GB. Expected throughput is around 38 tokens/second, which feels Fast — smooth conversation. Responses feel real-time. in interactive use. Meta's 8B parameter instruction-tuned model. Great balance of performance and efficiency for local deployment.

Setup tutorial: Llama 3.1 8B Instruct on M4 Pro

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

TL;DR

Llama 3.1 8B Instruct runs at Grade S on the Apple M4 Pro with FP16 quantization, achieving ~49 tok/sec. Requires 17.0GB VRAM, leaving ample headroom for large contexts.

Prerequisites

Before starting, ensure you have at least 20GB 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 FP16 quantization, you can expect the model to run at ~49 tok/sec, utilizing 17.0GB of VRAM. This leaves 31.0GB of VRAM for context, enabling you to handle large input sequences efficiently. Given the remaining VRAM, you can achieve a practical context window of up to 131072 tokens.

1. Install runtimeOllama (preferred on Apple Silicon)

brew install ollama
ollama init

2. Download the model

Download the FP16 quantized model (16.0GB file) from Hugging Face.

ollama pull bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Meta-Llama-3.1-8B-Instruct-f16.gguf

3. Run it

ollama run Meta-Llama-3.1-8B-Instruct-f16.gguf
ollama chat Meta-Llama-3.1-8B-Instruct-f16.gguf

4. Optimize for M4 Pro

To optimize performance on the Apple M4 Pro, ensure you are using the Metal/MLX backend to leverage the GPU's 48GB VRAM and unified memory architecture. Use the `--metal` flag during runtime to enable MPS layers, which can significantly speed up inference. With 17.0GB VRAM in use, you have 31.0GB of headroom for large context windows, allowing for efficient handling of long sequences.

Troubleshooting

Inference is slow or hangs.

Ensure the Metal/MLX backend is enabled with the `--metal` flag. If the issue persists, try restarting the Ollama service with `ollama restart`.

Out of memory errors.

Reduce the batch size or context length to fit within the 48GB VRAM limit. You can also try using a lower quantization level like Q8_0 if FP16 is too demanding.

Model not found after pulling.

Verify the model path and name. Ensure the model was successfully downloaded by checking the `~/.ollama/models` directory.

Alternative runtimes

While Ollama is the preferred runtime for Apple Silicon, you can also use LM Studio for a more user-friendly interface, llama.cpp for advanced customization, or MLX for direct Metal integration. Jan is another option but may not offer the same performance optimizations as Ollama on the Apple M4 Pro.

Full Llama 3.1 8B Instruct details →

Other models that run great on M4 Pro

FAQ (20)

What GPU do I need to run Llama 3.1 8B Instruct?

To run Llama 3.1 8B Instruct, you need a GPU with at least 5.1 GB of VRAM for the lowest quantization level, up to 17.0 GB for full precision.

Is Llama 3.1 8B Instruct good for coding?

Llama 3.1 8B Instruct is well-suited for coding tasks, offering a good balance of performance and efficiency for generating code and providing programming assistance.

Llama 3.1 8B Instruct vs Llama 3.1 8B?

Llama 3.1 8B Instruct is an instruction-tuned version of Llama 3.1 8B, making it better suited for following user instructions and generating more coherent and contextually relevant responses.

Can I run Llama 3.1 8B Instruct on a Mac?

Yes, you can run Llama 3.1 8B Instruct on a Mac with an M1 or M2 chip, provided you have the necessary VRAM and system resources.

How much VRAM does Llama 3.1 8B Instruct need?

Llama 3.1 8B Instruct requires between 5.1 GB and 17.0 GB of VRAM, depending on the quantization level used.

Is Llama 3.1 8B Instruct censored?

Llama 3.1 8B Instruct is not inherently censored, but it may include content filters to prevent harmful or inappropriate outputs.

Is Llama 3.1 8B Instruct commercial-use allowed?

Llama 3.1 8B Instruct is licensed under the llama3.1 license, which allows for commercial use, but you should review the specific terms to ensure compliance.

Llama 3.1 8B Instruct context length?

Llama 3.1 8B Instruct has a context length of 131,072 tokens, allowing it to handle very long sequences of text.

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