Can M4 Max run Whisper Tiny English (Quantized)?
Yes — runs locally
~102 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The M4 Max (128 GB VRAM) handles Whisper Tiny English (Quantized) comfortably using the Q5_1 quantization, which fits in 0.1 GB. Expected throughput is around 102 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Smallest possible speech recognition model. Only 32MB. English only. Default speech model - guaranteed to run on any iPhone.
Setup tutorial: Whisper Tiny English (Quantized) on M4 Max
AI-generated, GPU-specific. Verified commands for your exact hardware.
Whisper Tiny English (Quantized) runs at Grade S on the Apple M4 Max with Q5_1 quantization, achieving ~3274 tok/sec.
Prerequisites
Before starting, ensure you have at least 32MB of disk space available. This tutorial is designed for macOS Monterey 12.3 or later, with Xcode Command Line Tools installed. You can install Xcode CLT by running `xcode-select --install` in your terminal.
Expected performance
With the Q5_1 quantization, you can expect the model to run at ~3274 tok/sec, using only 0.1GB of VRAM. This leaves 127.9GB of VRAM available for context, allowing for a practical context window that can handle long audio inputs without performance degradation.
1. Install runtimeOllama (preferred on Apple Silicon)
brew install ollama
ollama init2. Download the model
Download the Q5_1 quantized version of the Whisper Tiny English model, which is a 0.0GB file.
ollama pull ggerganov/whisper.cpp:ggml-tiny.en-q5_1.bin3. Run it
ollama run ggerganov/whisper.cpp:ggml-tiny.en-q5_1.bin
ollama stream --model ggerganov/whisper.cpp:ggml-tiny.en-q5_1.bin --input 'Your audio file path here'4. Optimize for M4 Max
To optimize performance on the Apple M4 Max, leverage the Metal/MLX backend and unified memory. The 128GB VRAM allows for efficient handling of large contexts without swapping to system RAM. Ensure that MPS layers are enabled to take full advantage of the GPU's capabilities.
Troubleshooting
If you encounter an error related to missing MPS layers, ensure that your macOS version supports Metal Performance Shaders (MPS).
Update to the latest macOS version by running `softwareupdate --install --all`.
If the model runs but performance is below the expected ~3274 tok/sec, check if the Metal/MLX backend is properly configured.
Ensure that the Metal/MLX backend is enabled in your Ollama configuration by running `ollama config set backend metal`.
If you experience out-of-memory errors, even though you have 128GB VRAM, it might be due to other processes consuming VRAM.
Close unnecessary applications and services to free up VRAM. You can monitor VRAM usage with Activity Monitor.
Alternative runtimes
While Ollama is the preferred runtime for Apple Silicon, you can also use alternatives like LM Studio, llama.cpp, or MLX. LM Studio offers a more graphical interface and is useful for users who prefer a GUI. llama.cpp is a lightweight option for more advanced users who need fine-grained control. MLX is another viable choice, especially if you need to integrate the model into a larger application. Choose the runtime based on your specific use case and comfort level with the command line.
Other models that run great on M4 Max
FAQ (20)
What GPU do I need to run Whisper Tiny English (Quantized)?
Whisper Tiny English (Quantized) requires minimal GPU resources, needing only 0.1 GB of VRAM. It can run efficiently on most modern GPUs, including integrated graphics.
Is Whisper Tiny English (Quantized) good for coding?
Whisper Tiny English (Quantized) is primarily designed for speech recognition and may not be optimized for coding tasks. However, it can be useful for voice-to-text applications in development environments.
Whisper Tiny English (Quantized) vs Llama 3.1 8B?
Whisper Tiny English (Quantized) has only 0.039 billion parameters, making it much smaller and more resource-efficient compared to Llama 3.1 8B, which has 8 billion parameters. It is ideal for low-resource devices but less powerful for complex tasks.
Can I run Whisper Tiny English (Quantized) on a Mac?
Yes, Whisper Tiny English (Quantized) can run on a Mac. It is lightweight and compatible with macOS, requiring minimal system resources.
How much VRAM does Whisper Tiny English (Quantized) need?
Whisper Tiny English (Quantized) requires only 0.1 GB of VRAM, making it suitable for devices with limited graphics memory.
Is Whisper Tiny English (Quantized) censored?
Whisper Tiny English (Quantized) is not censored. It processes speech data as input without any content filtering or restrictions.
Is Whisper Tiny English (Quantized) commercial-use allowed?
Yes, Whisper Tiny English (Quantized) is licensed under the MIT license, allowing commercial use without restrictions.
Whisper Tiny English (Quantized) context length?
The context length for Whisper Tiny English (Quantized) is not explicitly defined, but it is designed to handle short speech segments efficiently.
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