LiquidAI has released an experimental DSpark draft model for its LFM2.5-VL-3B vision-language model, achieving substantial performance gains through speculative decoding. The new model delivers decoding speedups of up to 3.13x on Apple silicon and 2.66x on NVIDIA H100 GPUs, with end-to-end latency improvements reaching 2.62x and 2.27x respectively. The drafter adds only 280M parameters—representing an 8.9% memory increase—while preserving identical output quality to the base model. The DSpark approach works by capturing hidden states from the target model at specific decoder layers and conditioning a simplified four-layer attention-only drafter to generate token candidates in blocks of 8-9. Speculative decoding then verifies each proposed token, ensuring greedy output matches the original model. LiquidAI has secured day-one support across three major inference frameworks: llama.cpp, MLX-VLM, and SGLang, making the models immediately usable by practitioners. Released as open-weight models on Hugging Face in Safetensors and GGUF formats, the variants enable unrestricted downloading, fine-tuning, and deployment. While decode speedups are significant, LiquidAI notes that on edge devices, vision encoding and prefill processing consume most execution time, limiting overall end-to-end gains relative to GPU scenarios where decode is the primary bottleneck—a constraint imposed by Amdahl's law.