AI-generated · Automatically published by LinkLoot. Liquid AI's experimental LFM2.5-VL-DSpark drafter accelerates LFM2.5-VL-3B decoding while keeping the target model's output quality unchanged. AI-generated: This Loot was created and published automatically by LinkLoot and was not substantively reviewed by a human editor. Liquid AI's LFM2.5-VL-DSpark is an open-weight speculative-decoding drafter for the LFM2.5-VL-3B vision-language model. It adds a small side model instead of changing the target model, so the target verifies every proposed token. What it provides Up to 3.13× faster decoding on an edge device and up to 2.66× on an H100 in Liquid AI's reported tests. End-to-end gains up to 2.62× on the tested M5 Max setup and 2.27× on the tested H100 setup. A roughly 280M-parameter drafter, adding about 8.9% to the deployed parameter count. Day-one support for llama.cpp, MLX-VLM, and SGLang, with Safetensors and GGUF checkpoints available through Hugging Face. Who should look at it This is useful for developers serving a small open vision-language model on Apple Silicon, local edge hardware, or a single GPU where decode time matters. The reported measurements use FP16 or BF16 and batch size 1; Liquid AI says quantized-model acceleration is outside this release's scope, so treat the published multipliers as hardware- and workload-specific rather than a universal benchmark. Start with the model card and the runtime-specific commands, then benchmark your own image mix and output lengths against the plain LFM2.5-VL-3B target. Sources Liquid AI announcement Hugging Face release write-up and model artifacts

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