Can M4 Max run Kokoro 82M TTS?
Yes — runs locally
~102 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The M4 Max (128 GB VRAM) handles Kokoro 82M TTS comfortably using the ONNX-Q8F16 quantization, which fits in 0.6 GB. Expected throughput is around 102 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. High quality 82M parameter TTS model. Excellent speech synthesis with multiple voice options. 86MB download.
Setup tutorial: Kokoro 82M TTS on M4 Max
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run the high-quality Kokoro 82M TTS model on an Apple M4 Max with Ollama using the ONNX-Q8F16 quantization. Expect Grade S performance at ~3274 tok/sec.
Prerequisites
Before starting, ensure you have at least 1GB 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 ONNX-Q8F16 quantization, expect the model to run at approximately 3274 tokens per second, using around 0.6GB of VRAM. Given the 128GB VRAM, you have 127.4GB of headroom for additional context, allowing for a practical context window of several minutes of speech synthesis.
1. Install runtimeOllama (preferred on Apple Silicon)
brew install ollama
ollama init2. Download the model
Download the 0.1GB ONNX-Q8F16 quantized model from Hugging Face.
ollama pull onnx-community/Kokoro-82M-v1.0-ONNX:onnx/model_q8f16.onnx3. Run it
ollama run onnx-community/Kokoro-82M-v1.0-ONNX:onnx/model_q8f16.onnx
ollama chat --model onnx-community/Kokoro-82M-v1.0-ONNX:onnx/model_q8f16.onnx4. Optimize for M4 Max
For optimal performance on the Apple M4 Max, leverage the Metal/MLX backend to utilize the 128GB unified memory efficiently. Ensure that MPS layers are enabled to take advantage of the GPU's capabilities. The large VRAM allows for significant headroom even when the model uses 0.6GB.
Troubleshooting
Model does not load due to insufficient VRAM
Ensure you have at least 128GB of VRAM available and try restarting your machine to clear any unused memory.
Performance is below expected 3274 tok/sec
Check if the Metal/MLX backend is properly configured and ensure that MPS layers are enabled in your environment settings.
Ollama commands fail with permission errors
Run `sudo chown -R $(whoami) /usr/local/lib/ollama` to set the correct permissions.
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 low-level control, or MLX for direct Metal integration. Jan is another option but may require more manual configuration. Choose based on your specific needs and comfort level with the command line.
Other models that run great on M4 Max
FAQ (20)
What GPU do I need to run Kokoro 82M TTS?
Kokoro 82M TTS requires at least 0.6 GB of VRAM. Any modern GPU with this amount of VRAM should suffice.
Is Kokoro 82M TTS good for coding?
Kokoro 82M TTS is primarily designed for text-to-speech applications and not specifically for coding. However, it can be useful for generating spoken code snippets or documentation.
Kokoro 82M TTS vs Llama 3.1 8B?
Kokoro 82M TTS is a smaller, more focused model for text-to-speech with 82 million parameters, while Llama 3.1 8B is a larger, more versatile language model with 8 billion parameters, suitable for a wider range of tasks.
Can I run Kokoro 82M TTS on a Mac?
Yes, you can run Kokoro 82M TTS on a Mac as long as your system meets the minimum VRAM requirement of 0.6 GB.
How much VRAM does Kokoro 82M TTS need?
Kokoro 82M TTS requires 0.6 GB of VRAM to run smoothly.
Is Kokoro 82M TTS censored?
Kokoro 82M TTS is not inherently censored, but its output can be controlled through the input and configuration settings.
Is Kokoro 82M TTS commercial-use allowed?
Yes, Kokoro 82M TTS is licensed under the Apache-2.0 license, which allows for commercial use.
Kokoro 82M TTS context length?
The context length for Kokoro 82M TTS is currently unknown, but it is designed to handle typical text-to-speech inputs effectively.
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