Accelerating vision-language models with LFM2.5-VL-DSpark

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.

• Faster inference: decode speedups up to 3.13x on device and 2.66x on an H100, with end-to-end gains up to 2.62x and 2.27x.
• Small memory cost: the drafter adds 280M parameters, 8.9% on top of the 3B target
• Day-one support: LFM-compatible DSpark integrations for llama.cpp, MLX-VLM, and SGLang

How does speculative decoding work for VLMs

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.

Training and Architecture

We follow the DSpark recipe with a mixture of vision-language SFT data, weighted toward the workloads we expect the model to serve. Based on ablations across 3, 4, and 5 layers, the draft model is a simplified attention-only drafter with 4 layers and a block size of 9. We ran 10 epochs on the final mixture and measured acceptance after each, which improved with additional training tokens before reaching diminishing returns. At inference time, we recommend a block size of 8 or 9 depending on the hardware.

The resulting drafter has approximately 280M parameters and increases the deployed model’s parameter count by just 8.9%.

Component	LFM2.5-VL-3B
Decoder stack (4 layers)	193.0M
Hidden-state projection	21.0M
Markov head	65.5M
Norms + confidence head	6.4k
Total	279.5M