Flash-dLLM is a training‑free inference acceleration framework that improves the speed and memory efficiency of Diffusion Large Language Models (dLLMs). It tackles GPU memory I/O bottlenecks by introducing an I/O‑aware fused KV‑cache kernel and then employs a draft‑and‑verify decoding strategy that uses the dLLM itself as both drafter and verifier. Experiments on mathematical reasoning and code‑generation tasks show Flash‑dLLM outperforms existing acceleration methods, achieving up to 11.0× speedups over the Elastic‑Cache baseline.
By Quan Nguyen-Tri, Mukul Ranjan, Zhiqiang Shen
arXiv:2606. 26120v1 Announce Type: cross Abstract: Diffusion Large Language Models (dLLMs) offer a promising alternative to autoregressive models, excelling in text generation tasks due to their bidirectional attention mechanisms.
By Tianyi Wu, Xiaoxi Sun, Yanhua Jiao, Yulin Li, Yixin Chen, YunHao Cao, YiQi Hu, Zhuotao Tian
arXiv:2610.09311v1 Announce Type: new
Abstract: Diffusion language models (DLMs) generate text through iterative parallel refinement, offering the potential for higher throughput than autoregressive...
By Xinsong Feng, Peng Du, Zhizhuo Yang, Daniel M. Bikel, Jiayun Wang, Haipeng Chen
arXiv:2606. 13740v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) accelerate generation by denoising multiple tokens in parallel, making them attractive for latency-sensitive mobile inference.
By Tuowei Wang, Yanfan Sun, Ju Ren
arXiv:2607. 11211v1 Announce Type: new Abstract: The popularity of large language models (LLMs) escalates an ongoing demand for effective inference.
By Wenzong Yang, Danyang Zhang, Kun Cao, Tejus Siddagangaiah, Rajeev Patwari, Zhanxing Pu, Siyin Kong, Zijiang Yang, Hao Zhu, Varun Sharma, Yue Gao, Tianping Li, Fan Yang, Jicheng Chen, Yushan Chen, Fennian Zhao, Aaron Ng, Elliott Delaye, Ashish Sirasao, Sudip Nag
arXiv:2511. 21759v2 Announce Type: replace-cross Abstract: Diffusion-based large language models (dLLMs) have recently gained significant attention for their exceptional performance and inherent potential for parallel decoding.
By Linye Wei, Wenjue Chen, Pingzhi Tang, Xiaotian Guo, Le Ye, Runsheng Wang, Meng Li
arXiv:2609. 04010v1 Announce Type: new Abstract: Large Language Models (LLMs) owe much of their success to next-token prediction (NTP), but their autoregressive (AR) structure requires slow, sequential token generation.
By Subham Sekhar Sahoo, Lingjie Chen, Khiem Pham, Jonathan Geuter, Chaitanya Dwivedi, Varad Pimpalkhute, Yash Akhauri, Alexander Moreno, Mikhail Yurochkin, Zhenting Wang, Mostafa Elhoushi, Nolan Dey, Shane Bergsma, Joel Hestness, John Thickstun, Eric Xing, Zhengzhong Liu
The paper introduces SPECTRA, a runtime‑reconfigurable tiled architecture designed to accelerate speculative decoding for large language models on edge devices. SPECTRA adapts its compute engine within each tile between systolic GEMM execution and vector‑lane GEMV execution, while dynamically adjusting tile count, kernel partitioning, and communication patterns across tiles. Experiments on a 20‑tile FPGA prototype demonstrate up to a 2.09× speedup from tile‑level reconfiguration and an additional 1.25× improvement from system‑level adaptability compared to fixed designs.
By Gabriele Tombesi, William Baisi, Je Yang, Elisavet Lydia Alvanaki, Kevin Lee, Michael Lippe, Biruk Seyoum, Luca P. Carloni
arXiv:2606. 17104v1 Announce Type: cross Abstract: As large language models (LLMs) are increasingly deployed in latency- and cost-sensitive settings, inference efficiency has become a central systems challenge.
By Shun Usami, Venkatram Vishwanath, E. Wes Bethel
arXiv:2607. 04206v1 Announce Type: cross Abstract: Diffusion language models (dLLMs) generate text by iteratively denoising a masked response and can commit multiple output positions per model invocation.
By Nitin Kedia, Saurabh Agarwal, Myungjin Lee, Aditya Akella
arXiv:2606. 21633v2 Announce Type: replace Abstract: The KV cache dominates GPU memory in long-context LLM serving, crowding out batch capacity and leaving GPU compute idle.
By Omin Kwon, Doyeon Kim, Jongseok Park, Seung Yul Lee, Ion Stoica, Jae W. Lee
arXiv:2506. 06295v2 Announce Type: replace-cross Abstract: Autoregressive Models (ARMs) have long dominated the landscape of Large Language Models.
By Zhiyuan Liu, Yicun Yang, Yaojie Zhang, Junjie Chen, Chang Zou, Qingyuan Wei, Shaobo Wang, Yichen Zhu, Linfeng Zhang