Can M4 Max run Whisper Large v3?
Yes — runs locally
~102 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The M4 Max (128 GB VRAM) handles Whisper Large v3 comfortably using the Q8_0 quantization, which fits in 3.4 GB. Expected throughput is around 102 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Largest Whisper model. Best accuracy across all languages and accents.
Setup tutorial: Whisper Large v3 on M4 Max
AI-generated, GPU-specific. Verified commands for your exact hardware.
Whisper Large v3 runs at Grade S on the Apple M4 Max with Q8_0 quantization, achieving ~904 tok/sec.
Prerequisites
Before starting, ensure you have at least 3.4GB 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 Whisper Large v3 to achieve ~904 tok/sec, using 3.4GB of VRAM. Given the M4 Max's 128GB VRAM, you will have 124.6GB of headroom for context, allowing for very large context windows without running into memory constraints.
1. Install runtimeOllama (preferred on Apple Silicon)
brew install ollama
ollama init2. Download the model
Download the Q8_0 quantized version of Whisper Large v3 (2.9GB file) from Hugging Face.
ollama pull ggerganov/whisper.cpp:ggml-large-v3.bin3. Run it
ollama run ggerganov/whisper.cpp:ggml-large-v3.bin --input-path /path/to/audio/file.mp3 --output-path /path/to/output/transcription.txt4. Optimize for M4 Max
To optimize performance on the Apple M4 Max, ensure that the Metal/MLX backend is used to leverage the GPU's 128GB of unified memory. This will allow the model to run efficiently, utilizing the 3.4GB VRAM required for the Q8_0 quantization while leaving ample headroom for context data.
Troubleshooting
Model fails to load due to insufficient VRAM
Ensure that the Metal/MLX backend is enabled and that the Q8_0 quantization is used, which requires only 3.4GB of VRAM.
Performance is lower than expected
Check that the Metal/MLX backend is properly configured and that the model is running on the GPU rather than the CPU.
Audio input path not found
Verify that the path to the audio file is correct and accessible from the terminal.
Alternative runtimes
While Ollama is the preferred runtime for Apple Silicon, you can also use LM Studio, llama.cpp, or MLX. LM Studio provides a more graphical interface, llama.cpp offers more control over quantization, and MLX is useful for integrating with other machine learning workflows. Choose based on your specific needs and preferences.
Other models that run great on M4 Max
FAQ (20)
What GPU do I need to run Whisper Large v3?
To run Whisper Large v3, you need a GPU with at least 3.4 GB of VRAM. NVIDIA GPUs like the RTX 2060 or higher are recommended for optimal performance.
Is Whisper Large v3 good for coding?
Whisper Large v3 is primarily designed for speech recognition and not for coding tasks. It excels in transcribing audio and handling multilingual content.
Whisper Large v3 vs Llama 3.1 8B?
Whisper Large v3 has 1.55B parameters and is optimized for speech recognition, while Llama 3.1 8B has 8B parameters and is more suited for text generation and language understanding tasks.
Can I run Whisper Large v3 on a Mac?
Yes, you can run Whisper Large v3 on a Mac, but ensure your Mac has a compatible GPU with at least 3.4 GB of VRAM for smooth operation.
How much VRAM does Whisper Large v3 need?
Whisper Large v3 requires 3.4 GB of VRAM, regardless of quantization level, to run efficiently.
Is Whisper Large v3 censored?
Whisper Large v3 is not censored. It is designed to handle a wide range of audio inputs and transcribe them accurately without restrictions.
Is Whisper Large v3 commercial-use allowed?
Yes, Whisper Large v3 is licensed under the MIT license, which allows for both commercial and non-commercial use.
Whisper Large v3 context length?
The context length for Whisper Large v3 is not explicitly defined, but it is designed to handle long audio segments effectively.
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