✨LoPA: Scaling dLLM Inference via Lookahead Parallel Decoding
📝 Summary:
LoPA is a training-free algorithm enhancing dLLM inference parallelism by optimizing Token Filling Order. It achieves 10.1 tokens per forward pass for D2F-Dream, significantly boosting efficiency while maintaining performance. A multi-GPU system further accelerates throughput to 1073.9 tokens per...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16229
• PDF: https://arxiv.org/pdf/2512.16229
• Github: https://zhijie-group.github.io/blogs/lopa
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For more data science resources:
✓ https://t.iss.one/DataScienceT
#LLM #AI #Inference #ParallelDecoding #Performance
📝 Summary:
LoPA is a training-free algorithm enhancing dLLM inference parallelism by optimizing Token Filling Order. It achieves 10.1 tokens per forward pass for D2F-Dream, significantly boosting efficiency while maintaining performance. A multi-GPU system further accelerates throughput to 1073.9 tokens per...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16229
• PDF: https://arxiv.org/pdf/2512.16229
• Github: https://zhijie-group.github.io/blogs/lopa
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#LLM #AI #Inference #ParallelDecoding #Performance