5 papers
DAWN: Dependency-Aware Fast Inference for Diffusion LLMs
Lizhuo Luo, Zhuoran Shi, Jiajun Luo +4
Diffusion large language models (dLLMs) have shown advantages in text generation, particularly due to their inherent ability for parallel decoding. However, constrained by the qual…
Accelerating Parallel Diffusion Model Serving with Residual Compression
Jiajun Luo, Yicheng Xiao, Jianru Xu +5
Diffusion models produce realistic images and videos but require substantial computational resources, necessitating multi-accelerator parallelism for real-time deployment. However,…
Staleness-Centric Optimizations for Parallel Diffusion MoE Inference
Jiajun Luo, Lizhuo Luo, Jianru Xu +4
Mixture-of-Experts-based (MoE-based) diffusion models demonstrate remarkable scalability in high-fidelity image generation, yet their reliance on expert parallelism introduces crit…
FREE: Uncertainty-Aware Autoregression for Parallel Diffusion Transformers
Xinwan Wen, Bowen Li, Jiajun Luo +2
Diffusion Transformers (DiTs) achieve state-of-the-art generation quality but require long sequential denoising trajectories, leading to high inference latency. Recent speculative…
Beyond A Single AI Cluster: A Survey of Decentralized LLM Training
Haotian Dong, Jingyan Jiang, Rongwei Lu +5
The emergence of large language models (LLMs) has revolutionized AI development, yet the resource demands beyond a single cluster or even datacenter, limiting accessibility to well…