NVIDIA’s Vera Rubin architecture, unveiled at CES 2026 and expected to appear in cloud environments in the second half of the year, represents more than the next generation of GPUs. Rubin GPUs are intended to deliver approximately 5X the inference throughput of Blackwells at comparable precision levels. And the new HBM4 memory will provide much higher bandwidth than the current generation HBM3e.
But Rubin’s significance goes beyond performance gains. It marks a fundamental change in how AI infrastructure is built. Instead of treating GPUs as standalone accelerators, NVIDIA is moving toward a rack-scale “AI factory” model, where GPUs, CPUs, networking, storage, and software are designed to work together as a single system.
NVIDIA took this approach because the workloads AI systems must support are changing. Early infrastructure investments focused primarily on training models, where securing enough GPU capacity was the main challenge. Today, organizations are focused on running AI systems in production. According to HyperFRAME Research Lens data, 79% of organizations have already deployed Retrieval-Augmented Generation (RAG) or plan to do so in the next 12 months. Supporting these systems means infrastructure must serve many users simultaneously, maintain model context across long reasoning chains, and respond with low latency.
Rubin’s architecture is built for this shift. Its rack-scale structure and new memory tiers are intended to support large-scale production inference workloads. But those capabilities only matter if the infrastructure around the GPUs can support them. If the rest of the stack isn’t ready to support those workloads, the advantages of the new hardware will be limited.
For enterprises evaluating Rubin-era infrastructure, the key question is not just how many GPUs they can provision, but whether the rest of the stack can run production inference systems reliably at scale.
Before Vera Rubin arrives, there are six infrastructure questions every enterprise AI team should answer.
1. Are You Learning the AI Stack on Current-generation Systems?
Rubin-based cloud instances are expected in the second half of 2026. But the software stack that will run on Rubin is already available today on current-generation infrastructure.
Organizations experimenting with that stack now can begin learning how inference frameworks, orchestration layers, and memory systems behave in production. They can identify integration challenges early and refine their deployment approach before the next generation of hardware arrives.
Teams that wait until Rubin hardware becomes available to begin evaluation may find themselves starting several steps behind.
2. Have You Inventoried the Full AI Stack?
GPUs are the most visible part of AI infrastructure, but they are only one layer of the system. Running AI in production also depends on inference frameworks, model weights, storage systems, and deployment tools working together.
Before Rubin arrives, enterprises should understand what sits around their GPUs. Which inference framework are you using? Where are model weights stored? How is intermediate data – such as key-value (KV) cache – handled during inference?
Organizations that understand these layers on current-generation infrastructure will be better prepared to take advantage of Rubin when it arrives because the same AI stack will carry forward to the new hardware.
3. Where Does Lock-In Actually Occur?
When people talk about infrastructure lock-in, they often point to CUDA. In reality, most organizations already accept CUDA as part of using NVIDIA hardware.
Lock-in more often appears higher in the stack. Proprietary orchestration tools, cloud-specific SDKs, and managed inference pipelines can make workloads difficult to move between environments.
As organizations prepare for Rubin, it’s worth looking closely at each layer of the AI stack. Are your models portable? Is your orchestration tied to a specific platform? Could your inference workloads run somewhere else without being rewritten?
These questions can reveal dependencies that aren’t obvious until migration becomes necessary.
4. Have You Benchmarked the Right Deployment Environment?
The rack-scale design behind Rubin means the way an infrastructure environment is assembled can have as much impact on performance as the GPU itself.
Many benchmarks focus on raw GPU speed, but that doesn’t show how difficult it will be to run AI in production. A more useful test is to deploy an inference workload in different environments and compare how long it takes to get it running. How much engineering work is required? How many tools need to be configured? How much of the stack must be integrated by hand?
In practice, the time it takes to move from provisioning infrastructure to running production inference often reveals more about a deployment environment than any GPU benchmark.
5. Are You Deploying Incrementally?
Rubin is arriving alongside a rapidly evolving ecosystem of software frameworks, orchestration tools, and inference platforms. Making large architectural commitments too early can create unnecessary risk.
Instead of designing a full AI platform up front, many organizations benefit from starting with a smaller, clearly defined workload. Running a limited deployment in production allows teams to see how the infrastructure behaves, refine their deployment pipelines, and understand how their applications interact with the stack before expanding further.
This step-by-step approach makes it easier to adapt as Rubin-era infrastructure and software capabilities continue to mature.
6. Are Your Engineers Building Infrastructure or Building AI?
Rubin’s “AI factory” architecture brings together GPUs, CPUs, networking, memory, and storage as a tightly integrated system. Running that kind of environment can require significant engineering effort if teams are assembling the infrastructure stack themselves.
Organizations should consider where they want their engineers spending their time. Configuring orchestration layers, storage systems, and deployment tools can consume months of work.
In many cases, that effort could be better spent training models, optimizing inference, and building AI-powered applications. Infrastructure environments where the major components of the stack are already integrated can help teams take advantage of new architectures like Rubin much more quickly.
Vera Rubin Is Coming. The Time to Prepare Is Now.
Vera Rubin will deliver significant advances in AI infrastructure performance, but the enterprises that capture the most value will be those prepared to run the full stack that surrounds the hardware.
Moving from AI experimentation to production-scale inference requires more than GPUs. It requires orchestration frameworks, model layers, storage systems, and deployment pathways that work together as a cohesive environment.
Vultr is already surfacing this NVIDIA-native stack on current-generation Blackwell infrastructure, giving organizations a practical way to begin building that operational experience before Rubin arrives.
Organizations that start answering these questions now will be far better positioned to move from sandbox experiments to production AI systems when the Rubin generation becomes widely available.
Learn how Vultr is surfacing NVIDIA’s full AI stack – from Dynamo orchestration to Nemotron models – to help enterprises move from sandbox experiments to production-scale AI.
Read the white paper from HyperFRAME Research: From Sandbox to Scale: How Vultr is Surfacing the Entire Vera Rubin Stack.