The computing power demand brought by big models is turning "data center power consumption" from an engineer's topic to a public topic. To understand this matter, we need to distinguish between two types of electricity consumption: the training phase is a short-term, high-density concentrated consumption; The inference stage is a long tail consumption that continues to grow with the increase of demand. With the gradual popularization of AI applications, the proportion of inference electricity consumption will continue to rise.
There are roughly four practical paths to reduce consumption.
One is to improve chip and computing efficiency. More advanced processes, specialized AI acceleration chips, and low precision calculations such as FP8 and INT4 can significantly reduce energy consumption per unit of computing power. This is subtraction at the source, and it is also the fastest progressing path.
The second is to modify the heat dissipation method. The power density of chips continues to increase, and traditional air cooling is becoming increasingly difficult. Cold plate liquid cooling and immersion liquid cooling are beginning to be deployed on a large scale, and the PUE (Power Utilization Efficiency) index of data centers is also decreasing.
The third is to optimize operations and scheduling. Scheduling flexible tasks such as training and offline batch processing to execute during periods or regions with more abundant green power; Simultaneously improve server utilization and reduce idle waste. Software level optimization often yields faster results than replacing hardware.
The fourth is to introduce green electricity and waste heat utilization. Large data centers lock in clean power sources such as wind power and photovoltaics through long-term power purchase agreements, and some projects also use server waste heat recovery for park heating. These measures cannot achieve zero power consumption, but they can gradually decouple computing power growth from carbon emissions.
For most businesses and individuals, a more pragmatic attitude is to first calculate how much computing power they really need. If you can use APIs, don't build your own cluster. If you can reuse open source models, don't repeat training. Don't be lazy when it comes to caching, compression, and batch processing - every kilowatt hour saved is not only cost optimization but also environmental responsibility.
[Reference source] Comprehensive compilation of industry information released publicly (including International Energy Agency (IEA) related public reports, as well as publicly available information from major cloud and chip manufacturers)