arXiv:2607. 01984v1 Announce Type: cross Abstract: Newer lightweight convolutional neural networks are often presented as improving predictive performance and deployment efficiency, but such claims require controlled evaluation.
By Tasnim Shahriar
arXiv:2607. 19058v1 Announce Type: cross Abstract: Optimizer state is the largest single line item in the memory budget of mixture-of-experts (MoE) training: on a 6.
By Nuemaan Malik
arXiv:2605. 17106v2 Announce Type: replace-cross Abstract: Production LLM deployments increasingly maintain heterogeneous model pools spanning order-of-magnitude cost differences.
By Aashna Garg, Siddharth Singha Roy, Jinu Jang, Federico Brancasi, Shengyu Fu
arXiv:2608. 04448v1 Announce Type: cross Abstract: Diffusion transformers deliver strong image generation, but their training cost grows superlinearly with resolution.
By Seunghyun Ji
arXiv:2608. 09250v1 Announce Type: new Abstract: Federated learning (FL) must serve devices with varying computational capabilities.
By Bostan Khan, Masoud Daneshtalab
arXiv:2608. 07890v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models decouple total parameters from per-token compute, but deployment still requires storing every expert.
By Ali Janati, Kaoutar El Maghraoui, Xinyi Luo, Wenyuan Shen, Owen Zou, Yankai Mao