In an attempt to reveal business strategies in the AI sector, Chinese artificial intelligence sensation DeepSeek disclosed some financial figures stating that its theoretical profit margin might exceed five times expenses.

DeepSeek stated that its V3 and R1 inference models had an average node occupancy of 226.75.

A total of $87,072 was spent on operating each node, which consisted of eight Nvidia H800 GPUs (graphics processing units) that were rented for US$2 per GPU per hour.

The processing power, electricity, data storage, and other resources required to enable real-time AI model operation are referred to as inferencing.

In its GitHub disclosures, DeepSeek included a disclaimer, stating that its sales are significantly lower for a number of reasons, such as the fact that only a small portion of its services are monetized and that it provides discounts during off-peak hours. Additionally, the prices do not account for all of the R&D and training costs associated with creating its models.

Even if the staggering profit margins are purely speculative, the announcement coincides with a period when technology investors are particularly interested in the profitability of AI businesses and their models.

As they compete to create ever-more-advanced AI solutions, businesses ranging from OpenAI Inc. to Anthropic PBC are experimenting with a variety of revenue models, including subscription-based, usage-based, and license fee-based. However, these business models' return on investment is being questioned by investors, which has sparked a discussion about whether or not they can become profitable anytime soon.

In addition to providing an overview of its operations, the Hangzhou-based startup said on X that its online service had a "cost profit margin of 545 percent." It also explained how it optimized computing power by balancing load, which is the process of managing traffic so that work is distributed evenly among several servers and data centers. According to DeepSeek, the company innovated to manage latency, or the interval between a user's query submission and response, and maximize the volume of data processed by the AI model in a specified length of time.

In contrast to the proprietary strategy of its largest US competitors, such as OpenAI, the startup, which has advocated for open-source AI, shocked many in the field by revealing several significant discoveries and data supporting its models in a sequence of odd actions that started early this week.