arXiv:2507. 02964v2 Announce Type: replace-cross Abstract: The increasing scale of AI workloads demands High-Performance Computing (HPC) infrastructure and training methodologies that are both scalable and sustainable.
By Salahuddin Salahuddin, Ahmed Hussain, Jussi L\"opp\"onen, Toni Jutila
arXiv:2605. 09370v3 Announce Type: replace-cross Abstract: Large-scale AI training is now fundamentally a distributed systems problem, and hardware failures have become routine operating conditions rather than rare exceptions.
By Daemyung Kang, Eunjin Hwang, Hanjeong Lee, HyeokJin Kim, Hyunhoi Koo, Jeongkyu Shin, Jeongseok Kang, Jihyun Kang, Joongi Kim, Junbum Lee, Jungseung Yang, Kyujin Cho, Youngsook Song
arXiv:2606. 08173v1 Announce Type: cross Abstract: In sixth-generation (6G) networks, billions of cyber-physical systems (CPSs) - autonomous vehicles, smart grids, industrial robots, and remote-surgical equipment - will run over ultra-reliable low-latency slices, collapsing the gap between a remote breach and physical harm to milliseconds, a budget perimeter firewalls and centralised security operations centres cannot meet.
By Bilal Hussain, Muhammad Bilal, Tan Li, Haris Pervaiz, Xiao Tang, Qinghe Du, Fawad Ahmad, Muhammad Azhar, Jun Zhang
arXiv:2605. 25645v2 Announce Type: replace-cross Abstract: We present the first end-to-end demonstration of fine-tuning and serving Google's Gemma 4 31B model on TPU hardware, providing an empirical comparison of TPU and GPU platforms for large language model adaptation.
By Jatin Kishnani, Mayank Goel, Amit Singh, Pulkit Agrawal, Sairanjan Mishra
arXiv:2606. 20537v1 Announce Type: new Abstract: Mainstream LLM serving systems reuse prefix work mainly through paged or radix key-value (KV) caches.
By Liang Su
arXiv:2606. 24722v1 Announce Type: new Abstract: Frontier AI training is increasingly shaped by access to dense, centrally controlled accelerator clusters.
By Peter Toth