Market Potential

The market potential of GPU’s and its compute infrastructures depends on the market potential of Generative AI/ML models and AI language models like ChatGPT.

We can generally notice the immense growth of ChatGPT and Generative AI models market potential as these not only pave the way for natural language entry into automation but also challenges foundational platforms such as Google, Quora, Social Media and Stack Overflow by facilitating advanced conversational capabilities.

In order to train such advanced AI/ML models like ChatGPT an approximation of 10,000 H100 GPU machines are required. The petaflops of computing power produced by 10,000 H100 GPU is equal to (3,120 Petaflops). [One teraflop is equal to 1000 gigaflops, and one petaflop is equal to 1,000,000 gigaflops.)

Currently, there is an estimate of 100 prominent competitors of ChatGPT and each of these competitors would require 10,000 H100 GPUs for their AI initiatives, which indicates (100 X 10,000 = 1 Million H100 GPUs). Likewise there are an estimate of 10 large competitors to Bytedance's TikTok which acquired an estimate of 100,000 H100 GPUs from Nvidia as reported by techdator.net.

The overall market estimate of petaflops consumption needed by all these competitors would result into a huge sum of petaflops. (10 x 100K machines, 100 x 10K machines, 1000 x 100, 10000 x 100, 100K x 10, 1 Million x 1 Machine) resulting into an estimate of 10 - 100 Million machines for generative AI models. This highlights the surged demand for GPU machines and computational resources when it comes to powering these advanced AI technologies.

GPU.Net foresees the potential of the market, the increasing demand for generative AI and the need for high-performance GPU’s like the H100. This vast need powers our vision of being a Decentralized Uber for GPU compute, which would facilitate hassle free access to GPU computation for all future drives.