Can M3 Max run Whisper Large v3 Turbo?

Yes — runs locally

~102 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

128 GB

Model size

0.81B

Best quant

Q8_0

VRAM needed

2.0 GB

The verdict

The M3 Max (128 GB VRAM) handles Whisper Large v3 Turbo comfortably using the Q8_0 quantization, which fits in 2.0 GB. Expected throughput is around 102 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Optimized large Whisper model. Near-best accuracy with faster inference.

Setup tutorial: Whisper Large v3 Turbo on M3 Max

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

TL;DR

Whisper Large v3 Turbo runs at Grade S on the Apple M3 Max with Q8_0 quantization, achieving ~1629 tok/sec.

Prerequisites

Before starting, ensure you have at least 1.5GB of free disk space, macOS 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 Q8_0 quantization, you can expect ~1629 tok/sec with 2.0GB of VRAM in use, leaving 126.0GB of VRAM available for context. This allows for a practical context window of several minutes of audio, depending on the specific requirements.

1. Install runtimeOllama (preferred on Apple Silicon)

brew install ollama
ollama setup

2. Download the model

Download the Q8_0 quantized model (1.5GB) from Hugging Face.

ollama pull ggerganov/whisper.cpp:ggml-large-v3-turbo.bin

3. Run it

ollama run ggerganov/whisper.cpp:ggml-large-v3-turbo.bin
ollama stream --model ggerganov/whisper.cpp:ggml-large-v3-turbo.bin

4. Optimize for M3 Max

For optimal performance on the Apple M3 Max, leverage the Metal/MLX backend to utilize the 128GB unified memory efficiently. Ensure that MPS layers are enabled to take advantage of the GPU's parallel processing capabilities. With 128GB of VRAM, you have ample headroom for large context windows and batch sizes.

Troubleshooting

Low token throughput

Ensure that the Metal/MLX backend is enabled and that MPS layers are utilized. Run `ollama config set backend metal`.

Out of memory errors

Reduce the batch size or context window to fit within the available 126.0GB of VRAM. Adjust the model settings using `ollama config set batch_size <value>`.

Inference is slow

Check if the model is using the Metal/MLX backend. Run `ollama config set backend metal` to ensure optimal performance.

Alternative runtimes

While Ollama is the preferred runtime for Apple Silicon, alternatives like LM Studio, llama.cpp, and MLX can be used for specific needs. LM Studio provides a graphical interface, llama.cpp offers more control over quantization, and MLX is optimized for Metal performance. Choose based on your workflow and performance requirements.

Full Whisper Large v3 Turbo details →

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FAQ (20)

What GPU do I need to run Whisper Large v3 Turbo?

To run Whisper Large v3 Turbo, you need a GPU with at least 2.0 GB of VRAM. The exact VRAM requirement can vary slightly depending on the quantization level used.

Is Whisper Large v3 Turbo good for coding?

Whisper Large v3 Turbo is primarily designed for speech recognition tasks and may not be optimized for coding-related tasks. For coding, models like Codex or CodeLLaMa might be more suitable.

Whisper Large v3 Turbo vs Llama 3.1 8B?

Whisper Large v3 Turbo has 0.81 billion parameters and is optimized for speech recognition, while Llama 3.1 8B has 8 billion parameters and is more versatile for general language tasks. Choose based on your specific needs.

Can I run Whisper Large v3 Turbo on a Mac?

Yes, you can run Whisper Large v3 Turbo on a Mac as long as your Mac has a compatible GPU with at least 2.0 GB of VRAM. Ensure you have the necessary drivers and libraries installed.

How much VRAM does Whisper Large v3 Turbo need?

Whisper Large v3 Turbo requires at least 2.0 GB of VRAM. The exact amount can vary slightly depending on the quantization level used.

Is Whisper Large v3 Turbo censored?

Whisper Large v3 Turbo is not censored. It is an open-source model released under the MIT license, allowing for broad usage without content restrictions.

Is Whisper Large v3 Turbo commercial-use allowed?

Yes, Whisper Large v3 Turbo is licensed under the MIT license, which allows for commercial use without additional restrictions.

Whisper Large v3 Turbo context length?

The context length for Whisper Large v3 Turbo is currently unknown. Refer to the official documentation or model repository for the most accurate information.

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