Conclusion
The limitations of interconnects and GPUs are significant bottlenecks in AI training, which can lead to reduced training speed, increased power consumption, and decreased model accuracy. However, researchers and developers are exploring various solutions and innovations to address these challenges, including GPU aggregation, optimized data transfer, and new architectures. By addressing these challenges, we can unlock the full potential of AI and continue to drive the development of more powerful and accurate AI models.