The AI & Cloud-Native Infrastructure Blog

In today's fast-paced world of bioinformatics, the constant evolution of tools, dependencies, and operating system environments presents a significant challenge. Researchers often spend countless hours grappling with software installation, configuration, and version conflicts, hindering their ability to focus on scientific… Read More

As artificial intelligence and machine learning continue to evolve, one thing has become clear: not all infrastructure is created equal. GPUs were originally created for graphics rendering, but have evolved to play a crucial role in AI. To meet the… Read More

In today’s cloud-first world, understanding the differences between Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) is essential for IT decision-makers. These three core cloud models form the backbone of digital transformation,… Read More

What Is Platform as a Service (PaaS)? Platform as a Service (PaaS) is a cloud computing model, often referred to as the PaaS model, that provides a robust framework for developers to build, test, deploy, and manage applications efficiently. By… Read More

GPU Platform as a Service (GPU PaaS) is a cloud-native model that gives developers and data scientists secure, on-demand access to GPU resources for running AI, GenAI, and ML workloads.Rafay’s GPU PaaS™ stack simplifies GPU delivery across any environment—enabling faster… Read More

The GenAI revolution is in full swing, and for NVIDIA Cloud Partners (NCPs), GPU Cloud Providers (aka GPU Clouds), and Sovereign Cloud operators, it presents a significant opportunity. To keep up with market demands, NCPs and GPU Clouds are looking… Read More

The pressure to deliver on the promise of AI has never been greater. Enterprises must find ways to make effective use of their GPU infrastructure to meet the demands of AI/ML workloads and accelerate time-to-market. Yet, despite making significant investments… Read More

Enterprises are turning to AI/ML to solve new problems and simplify their operations, but running AI in the datacenter often compromises performance. Edge inference moves workloads closer to users, enabling low-latency experiences with fewer overheads, but it's traditionally cumbersome to… Read More

A surprising pattern is emerging in enterprises today: End-users building AI applications have to wait months before they are granted access to multi-million dollar GPU infrastructure.  The problem is not a new one. IT processes in most enterprises are a… Read More