Today, we release an experimental DSpark draft model for LFM2.5-VL-3B, bringing speculative decoding to our vision-language models. A lightweight drafter proposes multiple tokens ahead, and the target model verifies them together in a single pass. This accelerates generation without changing output quality. Across six vision-language task categories at batch size 1 and temperature 0: > MLX on M5 Max: up to 3.13x faster decoding and 2.62x end to end > llama.cpp on M3 Ultra: up to 2.14x faster decoding and 1.77x end to end > SGLang on H100: up to 2.66x faster decoding and 2.27x end to end All evaluations were collected using Pipette, the benchmarking infrastructure behind Liquid AI's public device-performance data. 🧵