Liquid AI ships a vision draft model, claiming decode speedups above 3x
A draft model adding only 8.9% parameters speeds up vision-model decoding, but end-to-end gains are capped by the vision-encoding stage; figures are vendor-measured.
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Liquid AI released LFM2.5-VL-DSpark on September 24, a speculative-decoding draft model for its 3B vision-language model LFM2.5-VL-3B. The company reports decode speedups up to 3.13x on device and 2.66x on an H100, with end-to-end latency improving up to 2.62x and 2.27x respectively.
The draft model has roughly 280M parameters, adding 8.9% to the deployed model's size, and ships with day-one support for llama.cpp, MLX-VLM, and SGLang. Speculative decoding is exact: the target model verifies every proposed token, so output is unchanged.
The company itself names the limit: speculation accelerates only decoding, not vision encoding or prefill, and on edge devices those stages take up more of the wall time, so end-to-end gains fall well below decode gains. All speed figures are the vendor's own measurements across six vision tasks.