Most text-to-speech systems have been judged on one thing: whether the voice sounds human. That matters, but it is less useful to a studio producing an audiobook or a game character, where the voice also has to laugh on cue and slow down when the scene calls for it.
Alibaba's new Qwen-Audio-3.1-TTS is built with that second group in mind. The model turns written text into speech across several languages and Chinese dialects, and it lets users shape the delivery with plain-language instructions. Tone, tempo and speaking style do not require technical parameters. A short description is enough.
Most talk about AI on phones has so far centred on the chatbot: a box you type into, an answer that comes back. That works for looking up facts. It is less useful for someone whose train has been cancelled and who now needs to rebook the journey, move a meeting and tell a friend they will be late.
That is the kind of problem Alibaba is aiming at with Qwen Intelligence. It is not a single app you download. It is a platform that smartphone makers can build into their own system assistants, so its AI functions sit inside the phone rather than on top of it. The stated goals are practical: replanning trips, adjusting appointments and creating images.
Running a 35-billion-parameter model on a single consumer graphics card usually means compromises. A new open source project called FastLocalAI tries to keep those compromises small. It's a tuned setup for running Alibaba's Qwen3.6-35B-A3B on an NVIDIA GPU with just 16 GB of VRAM, paired with the OpenCode coding agent.
The project is available on GitHub at https://github.com/24high/FastLocalAI. It uses llama.cpp's llama-server and moves part of the model into regular system memory so the rest fits on the card.
Why the model doesn't fit and how it works anyway
Qwen3.6-35B-A3B is a Mixture-of-Experts model. The quantized Q4_K_M version from Unsloth, which the project uses by default, is about 22 GB. That is too large for a 16 GB card.
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