How does speculative decoding work for VLMs Training and Architecture Inference Speedup on CPU and GPU Limitations of speculation for vision workloads How to use LFM2.5-VL-DSpark Get Started Citation Today, we release an experimental DSpark draft model for our vision-language model (VLM) LFM2.5-VL-3B. As with our recently released LFM2.5-DSpark drafter models, it adds a speculative decoding path that trades a minimal increase in memory footprint for a larger speedup without changing output quality.
The vision drafter uses the same architecture as our text LFM2.5-DSpark drafters: it captures the target model's hidden states at a fixed set of tapped layers and conditions on them to draft a block of k candidate tokens. Image patches and text tokens are projected into a shared representation before those layers, so the drafter operates on hidden-state vectors of identical dimensionality regardless of input modality. The inference algorithm is therefore unchanged from the text models.
