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Liquid AI has introduced an experimental speculative decoding drafter for its vision-language model that accelerates inference by up to 3.13x on Apple silicon without altering output quality. The approach leverages modality-agnostic hidden states to reuse text-model inference algorithms, with immediate support in major frameworks like SGLang and llama.cpp. This release highlights ongoing advancements in optimizing VLM efficiency through draft-target architectures rather than introducing new model families.

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Liquid AI Releases LFM2.5-VL-3B-DSpark: Speculative Decoding for Vision-Language Models With Up to 3.13x Faster Decoding

MarkTechPostSeptember 25, 20264 min read

Liquid AI has released LFM2.5-VL-3B-DSpark, a 279.5M-parameter draft model that brings speculative decoding to its LFM2.5-VL-3B vision-language model. It delivers up to 3.13x faster decoding on Apple M5 Max and 2.66x on H100, with identical output under greedy decoding. Support ships in llama.cpp, MLX-VLM, and SGLang. The post Liquid AI Releases LFM2.5-VL-3B-DSpark: Speculative Decoding for Vision-Language Models With Up to 3.13x Faster Decoding appeared first on MarkTechPost.

Liquid AI has announced LFM2.5-VL-3B-DSpark, an experimental speculative-decoding draft model for its LFM2.5-VL-3B vision-language model. The drafter adds about 280M parameters and speeds up decoding without changing the model’s output. Liquid AI team reports up to 3.13x faster decoding on Apple silicon and up to 2.66x on an NVIDIA H100. Is it deployable? Yes, Weights are live on Hugging Face in Safetensors and GGUF, with day-one support in SGLang, MLX-VLM, and llama.cpp. Liquid AI team labels the release experimental, and…

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