gpu paas
gpu paas
Stay updated with our expert articles and insights on cloud-native and AI infrastructure management and orchestration topics.
Rafay and Dell Technologies Forge a Faster Path to Production AI
Dell and Rafay are forging a faster path to production AI by delivering a powerful solution to help enterprises, telcos and neoclouds to build and scale sovereign AI platforms with confidence. With a full-stack approach and automation at its core, this joint offering supports innovation while ensuring operational control, compliance, data sovereignty and rapid ROI.
Compute Domains: Bringing Multi-Node NVLink Awareness to Kubernetes
Learn how compute domains and multi-node NVLink enable high-performance, distributed GPU workloads in Kubernetes, improving scalability, resource utilization, and AI infrastructure efficiency.
Scaling Trust: The Fortanix and Rafay Integration for Enterprise Confidential AI
Learn how the Fortanix and Rafay integration enables confidential AI for enterprises—protecting sensitive data while running AI workloads on secure, governed GPU platforms.
Rafay Launches AI Grid Orchestration Solution to Help Telcos Intelligently Deploy Distributed AI Infrastructure
Rafay, a member of the NVIDIA Inception program, brings infrastructure orchestration and workload automation to AI Grid architectures, enabling telcos and service providers to transform distributed GPU environments into a governed, self-service platform.
How GPU Clouds Deliver NVIDIA Run:ai as Self-Service with Rafay GPU PaaS
Learn how Rafay GPU PaaS enables GPU Clouds to offer NVIDIA Run:ai as a fully automated, multi-tenant managed service delivered through self-service with lifecycle management and turnkey deployment.
Simplifying AI Workload Delivery for Platform Teams in 2025
AI workloads are growing more complex by the day, and platform teams are under immense pressure to deliver them at scale—securely, efficiently, and with speed.
Why GPUs Are Essential for AI Workloads
As artificial intelligence and machine learning continue to evolve, one thing has become clear: not all infrastructure is created equal.
IaaS vs PaaS vs SaaS: The Cloud Computing Stack Demystified
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, each providing unique benefits and levels of abstraction.
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.
What is a GPU PaaS™?
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 time-to-market and maximum return on GPU investments, allowing you to immediately monetize your GPU (which historically are quite expensive and difficult to access).
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