Qwen3-TTS.cpp: Accelerated Local Inference

An optimized GGML (formerly llama.cpp) implementation has been released for the inference of Qwen3-TTS 0.6B, a text-to-speech model. This version, named Qwen3-TTS.cpp, aims to provide a more efficient alternative to PyTorch-based implementations, especially in contexts where computational resources are limited.

Performance and Optimizations

The implementation boasts a speed increase of up to 4x compared to the standard PyTorch pipeline, while maintaining a memory usage of approximately 2 GB. This improvement is achieved through the use of the Metal backend and the integration of a CoreML code predictor. The author notes that only some parts of the model were converted to take advantage of hardware acceleration, as other operations were not compatible with the ANE (Apple Neural Engine).

Features and Roadmap

The current version supports all the features of the original model, including voice cloning. Currently, quantization support is not yet available, but it is under development. Early tests with Q8 quantization have yielded unsatisfactory results, suggesting that some parts of the model are more sensitive to reduced precision than others.

Considerations for on-premise inference

Using implementations like Qwen3-TTS.cpp allows you to run inference directly on local hardware, offering greater control over data and reducing dependence on external cloud services. This approach can be particularly relevant in scenarios where data sovereignty and compliance with regulations such as GDPR are a priority.