vision-language-models.
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DSpark Vision Drafters Bring 3x Speedups to Edge VLMs
Speculative decoding for vision-language models achieves significant inference speedups on edge devices and GPUs with minimal parameter overhead and day-one framework support.
Speculative Decoding Brings VLMs to the Edge
Liquid AI's DSpark draft model achieves 3x faster vision-language inference on-device and 2.66x on H100s while adding just 8.9% parameters.
Speculative Decoding Brings VLMs to the Edge
Liquid AI's DSpark drafter achieves 3.13x speedups on-device by applying speculative decoding to vision-language models, with day-one support for llama.cpp and MLX.
Speculative Decoding Meets Vision: LFM2.5-VL Gets the Speed Treatment
Liquid AI's DSpark draft model accelerates vision-language inference by 2.3x on-device and 20x on GPU, but Amdahl's law reveals the real bottleneck.
Speculative Decoding Hits Vision Models: What Developers Need to Know
Liquid AI's DSpark draft model accelerates vision-language inference 2-3x on edge devices and 20x on H100s. Here's what it means for your stack.