Accelerating vision-language models using LFM2.5-VL-DSpark
| Source: Hugging Face | Original article
An experimental DSpark draft model, LFM2.5‑VL‑DSpark, has been released to accelerate the LFM2.5‑VL‑3B vision‑language model.
Liquid AI has unveiled an experimental “draft” model, LFM2.5‑VL‑DSpark, designed to speed up its 3‑billion‑parameter vision‑language model (LFM2.5‑VL‑3B). The new 280‑million‑parameter drafter sits alongside the original model and uses speculative decoding to generate candidate tokens before the full model validates them. According to the company’s release, the drafter adds just 8.9 % to the target model’s memory footprint while delivering decode‑time accelerations of up to 3.13 × on Apple M5 Max devices running MLX and 2.66 × on Nvidia H100 GPUs. End‑to‑end throughput improves by roughly 2.6 × on the edge and 2.3 × on the GPU.
The move follows Liquid AI’s earlier rollout of LFM2.5‑DSpark drafter models for pure‑text Liquid Foundation Models, which we covered on 24 September. By extending the speculative‑decoding approach to multimodal workloads, the company is tackling a key bottleneck: the latency and token‑cost of large vision‑language systems. LFM2.5‑VL‑3B, released on 12 August, targets tasks such as screen understanding, document reading, object grounding and tool use. Faster inference makes those use‑cases viable on consumer hardware and reduces cloud‑compute expenses, a trend echoed across the industry as developers prioritize efficiency over sheer scale.
What to watch next is whether Liquid AI publishes detailed benchmarks and opens the drafter to the broader community via Hugging Face, as hinted in a recent blog post. Further DSpark variants for other LFM2.5 models, as well as integration into on‑device AI stacks, could signal a shift toward lightweight, real‑time multimodal assistants. The next few weeks should reveal how developers adopt the draft model and whether the speed gains translate into measurable improvements in real‑world applications.
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