2 papers
cs.LG2026
Star Elastic: Many-in-One Reasoning LLMs with Efficient Budget Control
Ali Taghibakhshi, Ruisi Cai, Saurav Muralidharan +17
Training a family of large language models (LLMs), either from scratch or via iterative compression, is prohibitively expensive and inefficient, requiring separate training runs fo…
cs.AR2025
What Is Next for LLMs? Next-Generation AI Computing Hardware Using Photonic Chips
Renjie Li, Wenjie Wei, Qi Xin +7
Large language models (LLMs) are rapidly pushing the limits of contemporary computing hardware. For example, training GPT-3 has been estimated to consume around 1300 MWh of electri…