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[martinB78]

I know this has been written before but I can not reply to the topic too cause that is closed too.

there is a topic

Running vLLM-Omni for Qwen3-TTS(voice design, voice clone) on DGX Spark

Hello, This guide walks through setting up vllm-omni for running the Qwen3-TTS model family across different NVIDIA hardware platforms. Source: Qwen3-TTS - vLLM-Omni Create and activate a dedicated virtual environment using uv uv venv .vllm --python 3.12 source .vllm/bin/activate Install System Dependencies Audio processing requires ffmpeg and sox : sudo apt-get update sudo apt-get install ffmpeg sox -y Install vLLM (Platform-Specific) For x86_64 Machines (CUDA 13.0) uv pip install \ https…

that is already closed and I would just like to add a solution to it
I posted it in a similar thread but just like to add the link to it.

Support for Qwen3-TTS on DGX Spark (GB10) | torchaudio installation failure on ARM64

I spend some days to figure it out and then told the AI to write a tutorial for it. And here it is. Run Qwen3-TTS on DGX Spark GB10 with Voice Cloning (OpenAI-compatible API) I got faster-qwen3-tts running on the DGX Spark GB10 as a Docker container with an OpenAI-compatible TTS API. It works with OpenWebUI, SillyTavern, and any OpenAI TTS-compatible client. Uses CUDA graphs for 6-10x speedup over standard inference. Real-time factor is around 0.8x on the GB10 (faster than real-time). GitHub…

[MarkusHoHo]

Thanks for that Tutorial. I added a link to it in the original post you menitoned.