Liquid AI releases LFM2.5-VL-DSpark for faster vision-language model inference
Liquid AI announced LFM2.5-VL-DSpark, a draft model for its LFM2.5-VL-3B vision-language model. The model uses speculative decoding to speed up inference by up to 3.13x on-device and 2.66x on H100 GPUs, with end-to-end gains of 2.62x and 2.27x, respectively. It adds 280M parameters (8.9% increase) but maintains output quality, according to the company’s benchmarks across tasks like VQA,…
Key points
- LFM2.5-VL-DSpark speeds up vision-language model inference by up to 3.13x on-device and 2.66x on H100 GPUs
- Adds 280M parameters (8.9% increase) to LFM2.5-VL-3B with no output quality trade-off, per Liquid AI
- Supports day-one integration with llama.cpp, MLX-VLM, and SGLang for edge and GPU deployment
The model supports day-one integration with llama.cpp, MLX-VLM, and SGLang, targeting edge and GPU deployments. Liquid AI emphasizes its open-weight approach, allowing unrestricted fine-tuning and deployment. The release aligns with the lab’s goal of AI running across devices, from base models to specialized variants like audio and vision.
The story so far
4 episodes →- Liquid AI releases LFM2.5-VL-DSpark for faster vision-language model inferencethis story
Accelerating vision-language models with LFM2.5-VL-DSpark
Hugging Face · 24 September 2026
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