The appetite for AI compute resources continues to rise dramatically despite a hardware supply crunch. As supply constraints push up costs and squeeze the availability of AI compute, enterprises are looking beyond hyperscalers to neoclouds, specialized providers that deliver GPU and bare-metal capacity for AI workloads. Vultr, the largest independent cloud compute provider, is betting that its deep partnership with AMD will help it bring to market new hardware needed for inference and the next phases of AI.
Neoclouds are known a specialized cloud infrastructure provider that delivers GPU-as-a-service and bare-metal compute for AI training and inference workloads. While Vultr has been around for a quarter of a century, many see it as an independent hyperscaler that operates like a nimble, innovative neocloud.
"Demand is off the charts and there's such a unique time where there is far more demand than there is supply," Jason Yorn, vice president of global channel and alliance sales at Vultr, told The Forecast in a video interview recorded in July 2026 at the AMD Advancing AI event in San Francisco.
"That also causes a dramatic increase in cost to the end customer."
And therein lies what Yorn says is a key differentiator for Vultr: highly competitive price-to-performance offerings.
How Does a 25-Year-Old Independent Compete on AI Inferencing?
Vultr has spent a quarter century building the largest independent cloud compute platform, leaning on an AMD partnership to undercut hyperscalers on price and performance.
Florida-based Vultr has carved out a niche as the largest independent deliverer of cloud-based compute for both AI and common CPU workloads. The company operates more than 30 data center regions around the world. Its cloud compute platform is built largely on AMD technology, a partnership Yorn said gives customers a measurable edge.
"One of our latest, greatest SKUs is called VX1 that is again in partnership with AMD," Yorn said. "As compared to other solutions on the market, we're typically delivering anywhere from about 25 to 30% greater performance at a price point that's 30% less."
Price-to-performance matters more than ever to IT organizations with squeezed budgets. Not to mention the cost management challenges arising from using AI capabilities. According to IDC, many CIOs are realizing that standard budget forecasting doesn’t work for compute-heavy AI projects. Calculating the cost of GPUs, inference, networking and tokens is more complicated than planning a budget for a traditional IT system. CIOs must consider new security, governance and employee training costs.
Yorn says that turning to CPU compute resources whenever possible can free capital for the GPU-heavy work AI demands.
"Oftentimes that's a lever that allows customers to save money on compute costs today that helps them then fund their AI initiatives to grow their business," Yorn said.
Why Does Nimbleness Matter in the AI Hardware Race?
As AI hardware cycles accelerate, Vultr's speed in bringing successive AMD generations to market keeps it ahead of larger rivals.
Yorm said supply chain constraints are pushing many IT teams to move workloads into cloud services, where they can get reliable, scalable compute resources. Enterprises turn to Vultr not just for capacity but also for IT architecture solutions that must fit regional requirements.
"We oftentimes collaborate very, very closely with our customers to make sure that we're architecting the exact right solution to meet their needs," Yorn said.
That closeness extends to silicon, particularly cutting edge AMD chips. Yorn said Vultr has been first to market with successive AMD generations, from the MI300 to the MI325, and a launch partner for the 4005 series CPU. The company plans to be among the first to deliver the MI455X Helios rackscale solution and the upcoming Venice platform, moves that keep it on the leading edge of a rapidly changing market.
"Everything is changing and it changes really, really fast," Yorn said. "We remain very nimble. We are continuing to be on the leading edge of technology."
Video transcript:
Jason Yorn: We have really a phenomenal story. Vulture's been around for 25 years, and we are today the largest independent deliverer of cloud-based compute for both AI and common CPU workloads. We oftentimes get categorized as a neo-cloud, but again, we've been around for 25 years and we're anything but new in this space. And through our history, we are focused on serving SMB developers, mid-market, enterprise, AI native and hyperscale type customers. We have 33 data center locations around the globe serving customers of all shapes and sizes. Demand is off the charts and there's such a unique time where there is far more demand than there is supply. That also causes a dramatic increase in cost to the end customer. And that's one of the big value propositions that we bring to our clients is particularly on CPU compute workloads, being able to deliver customers far greater performance at a significantly lower price point as compared to some of our hyperscale competitors.
And oftentimes that's a lever that allows customers to save money on compute costs today that helps them then fund their AI initiatives. Now you look at some of the constraints that you see in supply chain today. Many customers who perhaps have been delivering solutions internally from a premise-based standpoint now are being forced to go into the cloud. And it's the limited supply that's in the market along with increased costs that are driving, again, far greater cloud adoption. And that's what positions us so well to support those customers. Again, whether it's regionally, whether it's globally, customers that have very unique workload type requirements or even security compliance requirements that we can meet again on a global basis. We oftentimes collaborate very, very closely with our customers to make sure that we're architecting the exact right solution to meet their needs. And we view that as one of the biggest competitive differentiators in our business as compared to many of the hyperscale competitors is just that level of intimacy, the level of collaboration that we offer our customers to make sure that we are architecting the right solutions to meet their specific needs.
Our cloud compute platform is largely built on AMD. We have really one of our latest, greatest skews is called VX1 that is again in partnership with AMD. And as compared to other solutions on the market, we're typically delivering anywhere from about 25 to 30% greater performance at a price point that's 30% less. So the ultimate realized benefit is in the range of about 80% better price to performance, which again brings tremendous value to our customers and that savings oftentimes helps them execute on other IT priorities, particularly in AI. Everything is changing and it changes really, really fast. And I think it's another testament to our business is we remain very nimble. We are continuing to be on the leading edge of technology with AMD in particular. As an example, we are going to be one of the very first in the world to deliver MI455 X Helios.We were one of the very first to deliver MI325, one of the very first to deliver MI300. We were the first to market a launch partner with the 4005 series CPU and we intend on being one of the very first to deliver Venice. So for us it's about being nimble, it's about being on the edge, and that allows us to differentiate and serve our customers best.
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Editor’s note: Learn about Nutanix Enterprise AI (NAI) 2.8, and the upcoming general availability of Nutanix Kubernetes Platform (NKP) 2.19, along with new incentives, programs, and resources designed to help partners accelerate growth on emerging AI opportunities in this press release: Nutanix Gives Enterprises the Freedom to Run Production Agentic AI Their Way.
Ken Kaplan is Editor in Chief for The Forecast by Nutanix. Find him on X @kenekaplan and LinkedIn.
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