Liquid AI adds a 280M draft model to speed up its 3B vision model
Ramin Hasani's team says the experimental add-on cuts decoding time by up to 3.13x on an M5 Max, while leaving image processing and prompt prefill unchanged.
By RuntimeWire Staff · Published
Primary source: Hugging Face Newsroom
Why it matters
Liquid AI is extending its device-first model strategy from smaller footprints to faster multimodal inference. The caveat is built into the benchmarks: DSpark speeds token generation, while image encoding and prompt prefill remain untouched, and quantized deployments were not tested.
Liquid AI released an experimental draft model for its LFM2.5-VL-3B vision-language model on September 24th, adding a 279.5-million-parameter component that the company says speeds up token generation without changing the target model's output distribution. The release extends the on-device strategy of co-founder and CEO Ramin Hasani (@ramin_m_h), whose team is trying to make multimodal models responsive on local hardware, where every extra pass through a large model costs time and memory.…