As the global artificial intelligence (AI) market thrives, graphics processing units (GPUs) have emerged as the central driving force of this revolution. Various applications driven by large language models (LLM) rely on these high-performance chips, and in the coming years, fluctuations in GPU prices could become more dramatic, necessitating new cost management skills for many businesses.
In some industries, cost fluctuations are not new. For instance, energy-intensive sectors like mining are accustomed to managing the ups and downs of energy costs, while logistics companies face fee fluctuations due to disruptions at the Suez and Panama Canals. However, industries such as finance and pharmaceuticals have little experience with this, yet they stand to benefit greatly from AI technologies and must adapt quickly.
Nvidia, a major GPU supplier, has seen its valuation soar rapidly this year. GPUs are highly sought after because they can process large amounts of calculations in parallel, making them ideal for training and deploying large language models. Some companies even need armored vehicles to transport these chips, underscoring their high demand. In the future, GPU cost fluctuations will be influenced by supply and demand dynamics.
As companies accelerate the deployment of AI applications, demand for GPUs is expected to increase significantly. Investment firm Mizuho predicts that the GPU market could expand tenfold over the next five years, exceeding $400 billion. Meanwhile, manufacturing capabilities and geopolitical factors will also affect supply, such as the threat to Taiwan's independence from China.
To cope with these fluctuations, businesses can adopt various strategies. Firstly, more companies may choose to manage GPU servers in-house rather than leasing from cloud service providers, despite the additional costs, as it can reduce costs in the long term. Additionally, companies might purchase GPUs in advance to ensure future inventory.
Moreover, the right type of GPU is crucial. For most businesses, data processing tasks running existing models do not require the most powerful GPUs; instead, lower-performance GPUs can optimize costs. Geographical location can also be a key to reducing costs, such as setting up GPU servers in regions with lower electricity costs, significantly cutting operational expenses.
However, the pace of development in AI computing is extremely fast, making it difficult for businesses to accurately predict their GPU needs. Therefore, companies should establish countermeasures early to adapt to potential price fluctuations in the future.
Key Points:
🌟 The GPU market is expected to expand tenfold over the next five years, reaching $400 billion.
⚡ Businesses need to choose the right GPU type to optimize costs and performance.
📈 Managing GPU servers in-house or purchasing in advance are effective strategies to cope with cost fluctuations.