Can M4 Pro run Whisper Large v3?
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 comfortably using the Q8_0 quantization, which fits in 3.4 GB. Expected throughput is around 90 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 Pro
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Whisper Large v3 on an Apple M4 Pro with Ollama using the Q8_0 quantization. Expect Grade S performance at ~339 tok/sec.
Prerequisites
Before starting, ensure you have at least 10GB 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 ~339 tok/sec processing speed and 3.4GB of VRAM usage. Given the 48GB VRAM, you have 44.6GB 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 whisper_large_v3 --model ggml-large-v3.bin
ollama interactive whisper_large_v34. Optimize for M4 Pro
For optimal performance on the Apple M4 Pro with 48GB VRAM, use the Metal/MLX backend to leverage the unified memory architecture. Ensure that MPS layers are enabled to take full advantage of the GPU's capabilities. With 48GB of VRAM, you have ample headroom for large context windows and additional tasks.
Troubleshooting
Error: 'MPS layers not enabled'
Ensure that Metal Performance Shaders (MPS) are enabled in your system settings. Run `defaults write com.apple.CoreML MLComputeUseMPS -bool YES`.
Low processing speed
Check if the Metal/MLX backend is correctly configured. Run `ollama config set backend metal`.
Out of memory errors
Reduce the batch size or context length to fit within the available VRAM. Use `ollama run whisper_large_v3 --batch-size 16` to adjust the batch size.
Alternative runtimes
Alternative runtimes include LM Studio, llama.cpp, and MLX. Use LM Studio for a graphical interface, llama.cpp for more control over quantization, and MLX for direct Metal integration. However, Ollama is generally preferred for its ease of use and optimized performance on Apple Silicon.
Other models that run great on M4 Pro
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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