New framework Seer accelerates DMLLMs by up to 31x via MLP sparsity

Researchers have developed a new framework called Seer that significantly accelerates the inference speed of Diffusion Multimodal Large Language Models (DMLLMs). By analyzing the MLP activation sparsity in the first denoising step, Seer can detect the valid semantic boundary of the output sequence. This allows for one-shot truncation of redundant padding, reducing unnecessary computation and increasing throughput by up to 31x. The framework maintains overall performance and even improves accuracy on certain visual tasks. AI

IMPACT Accelerates DMLLM inference, potentially enabling more efficient multimodal AI applications.

RANK_REASON Academic paper detailing a new method for accelerating LLM inference. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• Diffusion Multimodal Large Language Models
• DMLLMs
• DocVQA
• Hugging Face
• Seer

• paper
• infra

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