PH Deck

Users requiring high-performance computing for large AI models face limitations with older GPU solutions such as slower training times, higher costs, and limited scalability.

Users rely on traditional cloud GPU solutions for AI/ML workloads, facing high costs, limited scalability, and suboptimal performance for demanding compute tasks

Users relying on traditional cloud servers or older GPU solutions face inefficient handling of complex AI tasks and data operations due to limited computational power, slower processing times, and higher operational costs.

Users requiring high-performance GPUs for AI/ML, big data, and 3D rendering face high upfront capital expenditure, complex maintenance, and underutilization of owned hardware, leading to inefficiency and scalability challenges.

Users need to manage servers, which involves constant monitoring and task management.

The current approach often lacks real-time alerts and requires manual intervention.

Drawbacks include: the need for constant manual monitoring, delayed response to server issues, and inefficient task management.

Users require reliable and high-performance server hosting but face limitations with limited data center locations, opaque hardware specifications, and higher costs for lower-tier performance in traditional solutions.

Users manually analyze cultural trends and performative behaviors, which is time-consuming and often inaccurate. manually analyzing cultural trends and performative behavior

Users currently rely on manual checks or basic tools for monitoring NVIDIA GPU servers, leading to slow update intervals (e.g., minutes) and complex infrastructure setup

Users face high costs and limited access to NVIDIA GPUs with traditional cloud platforms, along with complex infrastructure setup and token-based pricing models that hinder scalable AI development.

Developers using Postman for API management and development face challenges in converting API endpoints into type-safe code, requiring manual effort and expertise.