Behind the Computing Power Shortage: The Dilemmas and Responses of Large Model Training

The training of large models is in full swing, but the shortage of high-end GPUs has become a major challenge faced by the industry. Despite prices continuously rising, the monthly rental fee for a top-tier GPU has reached 50,000 to 70,000 yuan, yet it is still hard to come by. This supply-demand imbalance is unlikely to ease in the short term, and major companies are calculating how much "inventory" they have on hand.

However, the threshold for training large models is not as simple as just acquiring GPUs. Taking a certain meteorological large model as an example, its training cost exceeds 2 million yuan. For general large models, it is difficult to sustain without an investment of billions. Some entrepreneurs describe the current competition in the large model field as "burning money"; without strong financial support, it is hard to persevere.

In the face of this dilemma, companies are also actively seeking countermeasures. Some methods include: using higher quality data to improve training efficiency; enhancing infrastructure capabilities to achieve long-term stable operation; optimizing Computing Power resource scheduling to improve utilization; adopting supercomputing architectures to replace cloud computing architectures, etc. Additionally, some companies choose to use domestic platforms for large model training and inference to replace the scarce imported GPUs.

In fact, Computing Power has already become a new service model. Computing Power services are based on diversified Computing Power, linked through a Computing Power network, aiming to provide effective Computing Power. It not only includes Computing Power but also encompasses the unified packaging of resources such as storage and networking. In this industry chain, upstream companies supply basic Computing Power resources, midstream companies are responsible for the production and supply of Computing Power, and downstream industry users rely on Computing Power services for value-added.

As the demand for high-performance computing by large models becomes normalized, Computing Power services are rapidly developing into a unique industrial chain and business model. Although the current issues of high-end GPU shortages and high costs still exist, in the long run, the service-oriented Computing Power is a certain trend. Computing Power service providers need to prepare in advance for market changes.