Performance bottlenecks in decentralized AI large language models like OLMo stem from several key challenges. Limited computational resources compared to centralized systems hinder training and inference efficiency, especially for models handling vast datasets like OLMo’s 5T-token corpus. Network latency and bandwidth constraints in decentralized setups slow down data sharing and model synchronization, impacting scalability. Training stability issues, such as loss spikes, can degrade performance, requiring sophisticated stabilization techniques. Additionally, data quality and diversity in decentralized environments may vary, affecting model robustness. OLMo 2’s advancements, like staged training and optimized post-training recipes, mitigate some issues, but fully open models still lag behind proprietary ones in resource-intensive tasks. Addressing these bottlenecks demands innovations in distributed computing, efficient data curation, and robust evaluation frameworks to ensure competitive performance.