Can M4 Pro run Whisper Large v3 Turbo?
Yes — runs locally
~90 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The M4 Pro (48 GB VRAM) handles Whisper Large v3 Turbo comfortably using the Q8_0 quantization, which fits in 2.0 GB. Expected throughput is around 90 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 M4 Pro
AI-generated, GPU-specific. Verified commands for your exact hardware.
Whisper Large v3 Turbo runs at Grade S on Apple M4 Pro with Q8_0 quantization, achieving ~611 tok/sec.
Prerequisites
Before starting, ensure you have at least 2GB 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 Q8_0 quantization, you can expect ~611 tok/sec with 2.0GB of VRAM in use, leaving 46.0GB of VRAM for context. This provides ample headroom to handle large context windows, making it suitable for long audio transcriptions.
1. Install runtimeOllama (preferred on Apple Silicon)
brew install ollama
ollama init2. Download the model
Download the Q8_0 quantized model (1.5GB) from Hugging Face.
ollama pull ggerganov/whisper.cpp:ggml-large-v3-turbo.bin3. Run it
ollama run ggerganov/whisper.cpp:ggml-large-v3-turbo.bin --device metal --threads 8
ollama interact ggerganov/whisper.cpp:ggml-large-v3-turbo.bin4. Optimize for M4 Pro
For optimal performance on the Apple M4 Pro, use the Metal/MLX backend to leverage the 48GB of unified memory. Set the number of threads to 8 to fully utilize the CPU cores. The MLX backend will offload some computations to the GPU, ensuring efficient use of the 48GB VRAM.
Troubleshooting
Low tokenization speed
Increase the number of threads using `--threads 8` in the run command.
Out of memory errors
Reduce the batch size or context length to fit within the 46.0GB of available VRAM.
Inference is slow
Ensure the Metal/MLX backend is enabled by adding `--device metal` to the 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 customization options, or MLX for direct Metal integration. Use LM Studio for ease of use, llama.cpp for advanced tuning, and MLX for maximum performance on Apple hardware.
Other models that run great on M4 Pro
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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