Domestic AI infrastructure and data center providers are hopeful that there will be greater business potential with the availability of low-cost open source DeepSeek model.

The very fact that Deepseek has been built at lower cost inspires the Indian startups and companies to build LLMs on similar lines using the least number of GPUs.

In addition, DeepSeek is open source. Startups can leverage its APIs at significantly lower costs.

There could be an increase in take-up of GPU as a service model. Compute providers can lease the GPUs at an hourly rate.

The open source models could be 10 times cheaper than working with the closed source models.

According to experts, DeepSeek follows a balanced loading approach while tackling a prompt. Models such as ChatGPT go through all the knowledge and put all the capabilities at one go. It increases the compute requirement. On the other hand, DeepSeek loads when required.

DeepSeek does not actually put all capabilities at one go while interacting with the users. It first understands the language and then whether the question is biological, medical, business or mathematical. It will then load the necessary knowledge. The approach is effective and requires less compute and is cost-effective. ChatGPT on the other hand answers any question by looking at a complete repository as it is trained on a single super-knowledge base. DeepSeek uses a two-geared approach.

Open source models lower entry barriers but increase at the same time the demand for infrastructure capable of supporting large scale inferencing and deployment. Thus, data centers play a critical role in AI revolution.

Such models require cutting edge algorithms and optimizations which enhance performance, keeping the costs under control.

DeepSeek architecture paves the way for more distributed and energy efficient data centers. They should provide flexible GPU leasing.