AI computing power warehouse (AICPW)

As the scale of large models (such as GPT, LLaMA, etc.) continues to expand, the computing power required for training and inference is growing exponentially. For example, training a large language model may require thousands or even tens of thousands of high-end GPUs (such as NVIDIA A100 or H100). However, the global chip supply chain has limited production capacity, leading to a shortage of computing resources. Especially for small and medium-sized enterprises or research institutions, obtaining sufficient computing power is costly, creating a "computing power gap."

The high demand for AI computing power brings a huge energy burden. Training a large model may consume millions of kilowatt-hours of electricity, equivalent to the annual electricity consumption of thousands of households. This high energy consumption not only increases operational costs but also puts pressure on the environment. With global attention on carbon neutrality, how to reduce the energy consumption of AI computing power has become an urgent issue.

Current AI computing power mainly relies on dedicated chips such as GPUs and TPUs, but the performance improvement speed of these chips is slowing down (Moore's Law is weakening). Although architectural optimizations (such as Transformer-specific accelerators) have alleviated the problem to some extent, the physical limits of hardware still restrict further expansion of computing power. Additionally, the complexity and cost of chip manufacturing are increasing.

To meet the demand for computing power, distributed training and inference have become mainstream, but this brings new challenges. For example, the synchronization efficiency of data parallelism and model parallelism, network bandwidth bottlenecks, and cross-node communication delays all affect overall performance. How to efficiently coordinate hundreds or even thousands of computing nodes remains a technical challenge.

In practical applications, the utilization of computing power for many AI tasks is not ideal. For example, some models may only use part of the hardware resources during the inference phase, and hyperparameter tuning during training may also lead to wasted computing power. Optimizing the match between algorithms and hardware is an area that requires continuous improvement.

Computing resources are unevenly distributed, mainly concentrated in developed regions such as North America, Europe, and East Asia, while other regions (such as Africa and South America) lack sufficient AI infrastructure. This imbalance limits the global development and application of AI technology.