time-to-first-audio (TTFA) is critical for realtime voice applications. open source implementations (e.g. vLLM-Omni, SGLang-Omni) are often too slow for production and can have issues with realtime playback if you push for lower latency. we wanted to fix that.
we optimized qwen3-tts, a popular OSS TTS model, to achieve 34 ms p95 TTFA at 10 requests per second on 1 x H100. we open source the implementation and benchmark, as well as a breakdown of how it was done.
Hi there! I actually thought your Dia models were amazing and very natural sounding, I haven’t tried qwen 3 tts yet - has your focus shifted away from building your Dia models and shifted more towards hosting and infrastructure?
Hey thank you for your kind words! Yes, we’ve shifted to inference but will also continue doing finetuning etc. on top of open models. Don’t have plans to do pretraining though.
The source text is just copy pasted from another comment in this page. I have no idea of the expected inputs to this kind of model, if new line chars etc are not expected.
We got a rtx 4090 handling around 10 concurrent requests at 50 ms TTFA after some config changes / adjustment as it doesn’t have FP8. So this 50 ms TTFA thing is very much possible on consumer hardware.
I can buy a used car for the price of a used RTX 4090 ($2500-$3000), I wouldn't consider it consumer hardware. Prosumer maybe. Almost no consumer needs 10 concurrent requests. How fast does this run on a 3060 or CPU/iGPU only like an Intel Iris or AMD Navi? Or is your priority more commercial cloud services instead of local self hosted?
I'm going to see how this shakes out on my machine with a few 3090s. I see you all are leveraging some custom cuda kernels, so it may not work out of the box on Ampere (30xx) architecture yeah?
we optimized qwen3-tts, a popular OSS TTS model, to achieve 34 ms p95 TTFA at 10 requests per second on 1 x H100. we open source the implementation and benchmark, as well as a breakdown of how it was done.
github: https://github.com/nari-labs/nari-qwen3-tts