Rafay Systems, a leading platform provider for AI infrastructure orchestration, operations, and monetization, today announced that the company's Virtual Machines-as-a-Service offering, a top-selling capability available to Rafay customers through the Rafay Platform, has been certified by NVIDIA for use with NVIDIA HGX systems, as well as NVL72 rack-scale systems.
The certification provides enterprises and cloud providers with greater confidence in using virtualization as part of production AI infrastructure. The NVIDIA-Certified Hypervisors program evaluates representative performance-critical behaviors across compute, memory, data-path efficiency, and large language model inference, helping organizations identify virtualization platforms and infrastructure software designed to support demanding AI and accelerated computing workloads.
"Virtualization is a key use case that AI Factory operators expect to leverage to address multi-tenancy requirements," said Haseeb Budhani, CEO and co-founder of Rafay Systems. "Achieving this certification under the NVIDIA-Certified Hypervisors program is yet another proofpoint to the community for Rafay's focus on making it easier for neoclouds and enterprises to govern and operate NVIDIA-powered AI Infrastructure at scale."
"Organizations are moving rapidly from acquiring GPUs to asking how those resources can securely support hundreds or thousands of users, applications, and AI workloads," Budhani continued. "Virtualization gives operators another important consumption model. Rafay provides the orchestration, governance, and self-service framework around those environments so infrastructure can become a scalable AI platform rather than a collection of isolated resources."
NVIDIA-Certified Hypervisors are virtualization-based solutions that have proven to deliver near bare-metal performance for representative AI and accelerated computing workloads by accurately exposing hardware topology and implementing key performance optimizations.
For Rafay customers, virtualization is one component of broader operating model for AI infrastructure. The Rafay Platform provides orchestration and governance across infrastructure and workload lifecycle, enabling organizations to deliver self-service virtualized AI infrastructure, support secure multi-tenancy with centralized identity RBAC quotas policies tenant isolation, expand infrastructure consumption models offering VMs alongside bare metal Kubernetes SLURM AI workbenches inference services from common platform, maximize return on accelerated computing investments turning idle underutilized accelerators into monetizable capacity driving up utilization revenue per GPU lowering cost, and create monetizable AI services packaging compute and AI environments into standardized offerings with metering chargeback service-based consumption.
The NVIDIA-Certified Hypervisors achievement builds on Rafay's broader work across NVIDIA ecosystem to help enterprises and infrastructure providers operationalize accelerated computing. Rafay, a member of NVIDIA Inception, has collaborated with NVIDIA spanning GPU Platform-as-a-Service reference architectures, NVIDIA-Certified Hypervisor provider, NVIDIA AI Cloud Ready, NVIDIA Cloud Partners, AI factories, NVIDIA DSX OS, NVIDIA Infra Controller, NVIDIA AI Enterprise software, and AI services delivered through Rafay Token Factory.
Together, these efforts address growing requirement across AI infrastructure market: making accelerated infrastructure not only available, but securely consumable and operationally ready for production workloads.
About Rafay Systems
Rafay Systems is a leading software provider powering the operators building the AI cloud, including NeoClouds, telecommunications providers, enterprises, and sovereign AI operators. The Rafay Platform lets these organizations operationalize GPU and compute infrastructure with self-service automation, governance, and multi-tenancy, spanning bare metal provisioning, infrastructure lifecycle management, virtual machines, Kubernetes, GPU PaaS, AI development environments, and Token Factory for publishing AI models as token-metered inference services. By simplifying orchestration and operations across this stack, Rafay helps operators increase GPU utilization and turn raw infrastructure into monetizable, production-ready AI services while maintaining security, consistency, and control.
