Can M4 Pro run Whisper Medium?
Yes — runs locally
~90 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The M4 Pro (48 GB VRAM) handles Whisper Medium comfortably using the Q8_0 quantization, which fits in 1.9 GB. Expected throughput is around 90 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Mid-size Whisper model. Strong multilingual speech recognition.
Setup tutorial: Whisper Medium on M4 Pro
AI-generated, GPU-specific. Verified commands for your exact hardware.
Whisper Medium runs at Grade S on the Apple M4 Pro with Q8_0 quantization, achieving ~636 tok/sec, making it ideal for high-performance speech recognition tasks.
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 using `xcode-select --install`.
Expected performance
With the Q8_0 quantization, expect ~636 tok/sec and 1.9GB VRAM usage, leaving 46.1GB 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 init2. Download the model
Download the Q8_0 quantized version of Whisper Medium (1.4GB) from Hugging Face.
ollama pull ggerganov/whisper.cpp:ggml-medium.bin3. Run it
ollama run ggerganov/whisper.cpp:ggml-medium.bin --device metal --quantization Q8_04. Optimize for M4 Pro
For optimal performance on the Apple M4 Pro, use the Metal/MLX backend to leverage the GPU's 48GB VRAM. The Q8_0 quantization reduces VRAM usage to 1.9GB, leaving ample headroom for large context windows. Utilize unified memory to ensure smooth data transfer between CPU and GPU.
Troubleshooting
Low tokenization speed
Ensure the Metal/MLX backend is enabled and the correct quantization is used: `ollama run ggerganov/whisper.cpp:ggml-medium.bin --device metal --quantization Q8_0`
Out of memory errors
Reduce batch size or context length to fit within the 1.9GB VRAM limit: `ollama run ggerganov/whisper.cpp:ggml-medium.bin --device metal --quantization Q8_0 --batch-size 16`
Model not found
Verify the model is correctly downloaded and available: `ollama list`
Alternative runtimes
Alternative runtimes include LM Studio, llama.cpp, and MLX. LM Studio is suitable for GUI-based workflows, while llama.cpp offers more customization options. MLX is ideal for low-level optimization but may require more setup. For the Apple M4 Pro, Ollama is generally the most straightforward and performant option.
Other models that run great on M4 Pro
FAQ (20)
What GPU do I need to run Whisper Medium?
To run Whisper Medium, you need a GPU with at least 1.9 GB of VRAM. NVIDIA GPUs such as the GTX 1060 or higher are recommended for optimal performance.
Is Whisper Medium good for coding?
Whisper Medium is primarily designed for speech recognition and is not optimized for coding tasks. For coding, models like Codex or CodeLlama are more suitable.
Whisper Medium vs Llama 3.1 8B?
Whisper Medium has 0.77 billion parameters and is specialized for speech recognition, while Llama 3.1 8B has 8 billion parameters and is a general-purpose language model. Llama 3.1 8B is better for text generation but requires more resources.
Can I run Whisper Medium on a Mac?
Yes, you can run Whisper Medium on a Mac. Ensure your Mac has a compatible GPU with at least 1.9 GB of VRAM and the necessary drivers installed.
How much VRAM does Whisper Medium need?
Whisper Medium requires at least 1.9 GB of VRAM to run efficiently. This can vary slightly depending on the quantization level used.
Is Whisper Medium censored?
Whisper Medium is not censored. It is an open-source model released under the MIT license, allowing for unrestricted use and modification.
Is Whisper Medium commercial-use allowed?
Yes, Whisper Medium is licensed under the MIT license, which allows for commercial use without any restrictions.
Whisper Medium context length?
The context length for Whisper Medium is not explicitly defined, but it is designed to handle typical speech segments effectively. For longer audio, you may need to split the input into smaller chunks.
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