Enterprise Account Executive

The AI infrastructure bottleneck: why 'good enough' Kubernetes isn't cutting it anymore. While security eyes are on the RSAC conference in San Francisco this week, the compute world is focused on KubeCon EU in Amsterdam. But the theme of artificial intelligence is the pervasive across both, as in enterprise information technology we've reached a point where "AI curiosity" has officially been replaced by "AI urgency." Every chief information officer I talk to is under immense pressure to move from those neat little research-and-development experiments to actual production-grade deployment. But as they scale, they're hitting a wall that isn't about the models or the data - it's about the plumbing. Specifically, it's the graphics processing unit infrastructure bottleneck. For years, we've treated Kubernetes as the panacea to infrastructure woes. Need to scale? Throw it in a container. Need to orchestrate? K8s is your friend. But when you're dealing with Nvidia Corp. Blackwell B300s and massive training clusters, the standard way of doing things is sharing overprovisioned environments or waiting weeks for dedicated hardware. These are recipes for project failure, only adding to the narrative that the majority of AI project fail. Today at KubeCon, neocloud provider QumulusAI and vCluster, creators of virtual Kubernetes cluster technology, announced a partnership to address much of the friction between infrastructure agility and the rigid demands of high-performance GPUs. The real cost of infrastructure friction. Today's reality is that enterprise development teams are currently stuck in a "pick your poison" scenario. * The wait-and-see approach: A dedicated GPU environment is requested, but the IT team needs time to provision and tells the requester to check back in three weeks. In the past, this has been an annoyance but in the AI race, three weeks is an eternity and could be the difference in being an industry leader or a laggard. * The Wild West approach: Business units share a massively overprovisioned environment. It's faster to get into, but it's a security nightmare, and resource contention makes training runs highly unpredictable and ever harder to forecast when attempting to capacity plan. This inefficiency is more than just an inconvenience; it's a massive drain on return on investment, since time is money. When companies deal with hyperscalers or neocloud providers, they expect the kind of speed that Nvidia Blackwell B300s and RTXPRO 6000s promise. Having those chips sit idle while a developer fumbles a namespace configuration is the compute version of malpractice. QumulusAI and vCluster: partitioning power. The partnership between QumulusAI and vCluster brings customers a way to "slice and dice" high-end GPU power without the overhead of traditional virtualization. This gives customers more options but more importantly, the exact amount of GPU power they need to run their accelerated computing workloads, the primary one being AI. QumulusAI came to market with a value proposition of building a turnkey, vertically integrated AI cloud. Think of QumulusAI as a company that didn't just build a fast car, but designed the engine, the fuel and the highway it runs on. This "hyperspeed compute" setup provides massive power, but QumulusAI also provides the dashboard to keep all the horsepower under control. In fact, the company will let customers only use a piece of the engine if that's all that's required for the journey. By integrating vCluster's virtual Kubernetes technology, QumulusAI is essentially giving enterprises faster and more granular control of isolated environments. Instead of spinning up an entire physical cluster for every project, which is slow and expensive, teams can now spin up isolated virtual clusters on shared GPU hardware. This gives developers the "feel" of a dedicated environment - complete with their own application programming interface server and full control - while the platform team gets to maximize the utilization of those incredibly expensive GPUs. The vCluster AI Lab: innovation at the edge. Perhaps the most interesting part of this news is the launch of the vCluster AI Lab. The lab should provide QumulusAI customers assurance they can continue to use the platform for the long term. As the physical chips that are used for AI, such as GPUs, rapidly improve, the software managing them must stay ahead of the curve. This lab ensures that no matter how advanced the hardware becomes, the systems can handle the workload. It allows vCluster engineers to prototype how Kubernetes should handle emerging AI workloads in real time. Accelerating the move to AI factories. As I've noted in my previous posts, in 2026 the goal for companies should be to move AI factories from being projects to production infrastructure. To get there, organizations need three things: * Access: Getting the latest silicon (such as the B300) without a two-year lead time. * Isolation: Ensuring that Team A's training run doesn't crash Team B's inference model. * Speed: Moving from idea to environment in minutes, not months. This partnership addresses all three points and allows a midsized enterprise to act like a large company and enterprises to act like hyperscalers. They get the security of an isolated environment and the performance of bare-metal GPUs, all managed through a unified Kubernetes stack. Final thoughts. The AI race is going to be won by the companies that solve the operational headaches of GPU management. The technology is there, but can organizations deploy it in a way where it meets their needs now, doesn't break the bank and can scale with them? The partnership between QumulusAI and vCluster lowers the barrier to entry for secure, high-performance environments and makes it possible for AI teams to move as fast as their ideas. And in today's market, speed isn't just an advantage - it's the only thing that matters. Zeus Kerravala is a principal analyst at ZK Research, a division of Kerravala Consulting. He wrote this article for SiliconANGLE. Image: QumulusAI. A message from John Furrier, co-founder of SiliconANGLE: Support our mission to keep content open and free by engaging with theCUBE community. 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vCluster Labs has introduced vMetal, a bare metal machine management layer designed to help neocloud providers and AI factories provision and operate GPU infrastructure at scale. The platform automates the lifecycle of bare metal GPU servers, from provisioning and assignment to upgrades and repurposing. Combined with vCluster's tenant orchestration and AI-focused Certified Stacks, the vCluster Platform delivers what the company calls the industry's first unified AI infrastructure stack. The platform spans physical machines, Kubernetes environments and AI-ready application stacks, deployable across public cloud, private data centres and GPU neocloud providers. The initial Certified Stacks feature deep integration with NVIDIA Run:ai, which has validated vCluster as Run:ai conformant. The new release is available now.

vCluster Labs introduces Infrastructure Tenancy Platform for AI to maximize NVIDIA GPU efficiency on Kubernetes environments. New platform provides a Kubernetes-native foundation for running AI workloads on NVIDIA AI infrastructure, combining advanced isolation, dynamic scaling, and hybrid networking. ATLANTA (KubeCon + CloudNativeCon North America 2025, Booth #421) - November 10, 2025 - vCluster Labs, the company pioneering Kubernetes virtualization, today announced its Infrastructure Tenancy Platform for AI to help organizations build and operate high-performance AI infrastructure on GPU-focused compute clusters, including support for NVIDIA DGX systems. The company's new Reference Architecture for NVIDIA DGX systems is now available, offering architectural guidance for building secure, scalable Kubernetes environments optimized for NVIDIA AI infrastructure. Alongside this, vCluster introduced several new technologies, including vCluster Private Nodes, vCluster VPN, the Karpenter-based vCluster Auto Nodes feature, and direct integrations with NVIDIA Base Command Manager, KubeVirt, and the network isolation controller Netris, all of which form the foundation of the vCluster Infrastructure Tenancy Platform for AI, a unified framework for deploying and managing AI workloads on AI supercomputers in the private cloud as well as on top of hyperscalers and emerging neoclouds. "Our mission is to make AI infrastructure as dynamic and efficient as the workloads it supports," said Lukas Gentele, CEO of vCluster. "With our Infrastructure Tenancy Platform for AI, organizations running NVIDIA AI infrastructure can operate secure, elastic Kubernetes environments anywhere, with the performance, control, and efficiency that AI-scale workloads demand. It feels like getting the most cutting edge public cloud managed Kubernetes but on your bare metal AI supercomputer." Building blocks for the AI infrastructure era. As enterprises race to operationalize AI at scale, platform teams need a Kubernetes foundation that can manage GPU resources efficiently while ensuring workload isolation, mobility, and security. The Infrastructure Tenancy Platform for AI addresses these challenges through the following key innovations: * vCluster Private Nodes & Auto Nodes - Enable virtual clusters to dynamically autoscale GPU and CPU capacity across clouds, data centers, and bare metal environments using Karpenter-based automation. These features help maximize GPU utilization while maintaining full isolation and flexibility. * vCluster VPN - A Tailscale-powered overlay network that establishes secure communication between control planes and worker nodes across hybrid infrastructure. vCluster VPN simplifies burst-to-cloud scenarios, where GPU clusters seamlessly extend from on-premises NVIDIA DGX systems to public cloud environments. * NVIDIA Base Command Manager Integration - Integrates vCluster with NVIDIA Base Command Manager to bring Auto Nodes to NVIDIA DGX clusters, enabling elasticity, GPU lifecycle management, and efficient scaling across on-prem NVIDIA infrastructure. * KubeVirt Integration - Enables the creation of virtual machines on demand as nodes within a virtual cluster using KubeVirt, allowing large bare-metal servers to be partitioned into smaller, isolated compute units. This extends Auto Nodes to on-prem and bare-metal environments, giving platform teams elastic, tenant-aware GPU infrastructure under Kubernetes. * Netris Integration - Provides automated network isolation and lifecycle management for virtual clusters, giving each tenant its own dedicated network path and enabling multi-tenant GPU environments to run securely on shared infrastructure. * vNode Runtime - A secure, Kubernetes-native container sandbox that helps prevent container break-outs, enabling multi-tenant GPU workloads without reverting to VMs. Together, these technologies create the foundation of the vCluster Infrastructure Tenancy Platform for AI - a composable, Kubernetes-native framework purpose-built for running AI, ML, and GPU-intensive workloads anywhere. Industry analysts are increasingly highlighting the urgency of optimizing GPU utilization and simplifying AI infrastructure management. "As AI infrastructure becomes the new competitive frontier, organizations are under immense pressure to operationalize GPUs efficiently while maintaining security and governance across hybrid environments," stated Paul Nashawaty, Practice Lead and Principal Analyst at theCUBE Research. "We find that 71% of enterprises cite GPU utilization inefficiency as a major barrier to scaling AI workloads, and nearly two-thirds are exploring Kubernetes-native approaches to unify AI operations across cloud and on-prem. vCluster Labs' Infrastructure Tenancy Platform for AI directly addresses this gap by enabling dynamic, multi-tenant GPU orchestration with the same elasticity and control enterprises expect from the public cloud, now extended to private NVIDIA-powered AI systems." The new vCluster Reference Architecture for NVIDIA DGX systems outlines best practices for deploying virtual clusters on gpu-centric systems, enabling enterprises to deliver a cloud-like Kubernetes experience on-premises. With vCluster, teams can create lightweight virtual clusters that autoscale GPU resources, integrate securely with both on-prem and cloud networks, and maintain consistent performance across environments. "We've been using vCluster for a while and we love the technology," said Nick Jones, VP of Engineering at Nscale. "We're using vCluster to optimise GPU utilisation and accelerate Kubernetes cluster provisioning - delivering higher performance and efficiency that directly benefit our customers." Enabling cloud agility for NVIDIA GPU infrastructure. From AI factories to private GPU clouds, vCluster brings the scalability and efficiency of public cloud Kubernetes to NVIDIA environments. * Faster cluster provisioning - virtual clusters spin up in seconds with fully declarative provisioning via Terraform and GitOps * Higher GPU utilization - fewer idle GPUs across teams and tenants while ensuring fair use for everyone across the organization * Simplified day 2 operations - automated control plane and node upgrades, automatic backups with vCluster Snapshots and standardized guidance for integration into common cloud-native observability stacks Experience vCluster at KubeCon North America. Be among the first to experience the vCluster Infrastructure Tenancy Platform for AI at KubeCon + CloudNativeCon North America 2025 in Atlanta. Visit Booth #421 for live demos, technical sessions, and book signings. vCluster is also a Diamond Sponsor of Cloud Native + Kubernetes AI Day, where company leaders will present live sessions on GPU-accelerated Kubernetes operations, followed by a fireside chat featuring speakers from NVIDIA, JPMorgan Chase, and vCluster on "The Future of AI and Kubernetes."