Can M3 Max run Llama 3.1 8B Instruct?

Yes — runs locally

~48 tok/sec · Fast — smooth conversation. Responses feel real-time.

Your VRAM

128 GB

Model size

Best quant

FP16

VRAM needed

17.0 GB

The verdict

The M3 Max (128 GB VRAM) handles Llama 3.1 8B Instruct comfortably using the FP16 quantization, which fits in 17.0 GB. Expected throughput is around 48 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 M3 Max

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

TL;DR

Llama 3.1 8B Instruct runs at Grade S on the Apple M3 Max with FP16 quantization, achieving ~132 tok/sec.

Prerequisites

Before starting, ensure you have at least 16GB 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`.

Expected performance

With the FP16 quantization, you can expect ~132 tok/sec and 17.0GB of VRAM in use, leaving 111.0GB of headroom for context. This allows for a practical context window of up to 131,072 tokens, making it suitable for long-form text generation and complex tasks.

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

4. Optimize for M3 Max

For optimal performance on the Apple M3 Max, use the Metal/MLX backend to leverage the 128GB of unified memory. Ensure that MPS layers are enabled to take full advantage of the GPU. The large VRAM allows for a high context window, which is beneficial for complex tasks.

Troubleshooting

Low token generation speed

Ensure that the Metal/MLX backend is enabled and that MPS layers are utilized.

Out of memory errors

Reduce the context length or batch size to fit within the available VRAM.

Model not found

Verify that the model was successfully downloaded and is correctly referenced in the `ollama run` command.

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. Jan is another option but may not offer the same level of optimization for Apple M3 Max.

Full Llama 3.1 8B Instruct details →

Other models that run great on M3 Max

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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