Fast-dVLM is a new vision-language model that uses block-diffusion for parallel decoding, significantly speeding up inference on edge devices compared to traditional autoregressive models. This advancement is crucial for applications like robotics and autonomous driving where real-time processing efficiency is critical, as it leverages existing multimodal capabilities while reducing latency and improving hardware utilization.
Read the full article at arXiv cs.CL (NLP)
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