Chips & Supply · United States

Nvidia GPUs are everywhere. Here are the ways companies are accessing them

Credited to CNBC · cnbc.com · 6h ago

Marc Benioff, chief executive officer of Salesforce Inc., left, and Jensen Huang, chief executive officer of Nvidia Corp., during the 2026 Dreamforce conference in San Francisco, California, US, on Tuesday, Sept. 15, 2026.

David Paul Morris | Bloomberg | Getty Images

Nvidia GPUs are the most sought-after processors in AI, and they're in such demand that the chipmaker's stock climbed to yet another record this week, lifting its market cap close to $6 trillion.

Customers can now shop around for access to the chips at the giant clouds from Amazon, Microsoft and Google, as well as at so-called neoclouds like CoreWeave. They can also go to various online marketplaces or even buy the costly hardware directly.

For Nvidia, it all adds up to unrelenting growth, as management anticipates $108 billion in revenue for the October quarter, which would mark an 89% year-over-year jump.

But the paradox of choice can be a headache for companies needing computing power yesterday.

While cloud infrastructure providers have ranked at the top of Nvidia's customer list for several years, the business is diversifying. Five clients accounted for at least 10% of Nvidia's accounts receivable in July quarter, up from three in January, according to a filing.

Industry research firm SemiAnalysis counted 323 Nvidia GPU providers as of September, up from 209 less than 11 months earlier.

"You're going to see a whole new crop of really, really exciting neoclouds with hundreds of billions of dollars backlog together," Nvidia CEO Jensen Huang said at a Goldman Sachs tech conference in San Francisco last month.

Here's a rundown of the various options for accessing GPUs, and why each might make sense:

Hyperscalers

Many big companies spend tens of millions of dollars per year on a smorgasbord of cloud services from Amazon, Google and Microsoft. Since the 2022 launch of ChatGPT, companies have increasingly turned to the hyperscalers for GPUs so they can run generative AI workloads.

The top cloud providers come with a reputation advantage. If a software company relies on Amazon and Microsoft for GPUs and other capabilities, it won't need to panic about prospective customers questioning its suppliers.

Microsoft Azure signage on the exhibition floor during the Nvidia GTC conference in San Jose, California, on March 18, 2026.

"When you're talking to enterprises, your subprocessor had better be Azure," said Bindu Reddy, CEO of AI assistant startup Abacus, referring to Microsoft's cloud infrastructure.

In the past year, leading AI labs Anthropic and OpenAI have committed to spending over $500 billion between Amazon and Microsoft, which controlled 59% of the cloud infrastructure market in 2025, according to industry researcher Gartner.

"Hyperscalers are in a good position to show trust to the enterprises because of their 10-plus years of full-stack capabilities," said Gartner analyst Hardeep Singh. But hyperscalers don't always have as many GPUs as enterprises require, he said.

Andy Jassy, Amazon's CEO, told analysts in July that the retailer and cloud pioneer won't be able to serve all the demand it foresees this year.

"I believe this dynamic will also be true in 2027," he said.

Flagship neoclouds

If the hyperscalers were adequate, neoclouds wouldn't be proliferating.

Modal, a startup operating virtual sandboxes where AI agents work independent of main IT environments, went from running on the hyperscalers to signing up with the major neoclouds, and now it uses 25 of them, said CEO Erik Bernhardsson.

"You can get a few hundred GPUs or maybe a thousand, but at our scale, we needed way more GPUs," he said.

The hyperscalers themselves are also chasing the neoclouds. Google and Microsoft have started tapping CoreWeave, even as they all compete with one another.

"Some of the hyperscalers have approached us about taking care of customers they're worried about because they don't have the ability to service those customers when they need it," said Marc Boroditsky, chief revenue officer of Nebius, a Netherlands-based neocloud with operations in the U.S.

Video generation startup Reactor uses GPUs through Nebius and hyperscalers, said CEO Alberto Taiuti. The location of data centers matters, he said, because Reactor wants user-created videos to show up right away. Nebius delivers the specific GPUs Reactor needs, solid customer service and sufficient hardware and software, at a good price, Taiuti said.

Michael Intrator, co-founder and CEO of CoreWeave, speaks during an interview with CNBC on the floor at the New York Stock Exchange (NYSE) in New York City, U.S., Feb. 27, 2026.

The most visible neoclouds can require some upfront payment, and chips might not come online for months, because providers raise funding based on contracts and set up the data center equipment, Bernhardsson said.

It would be difficult for CoreWeave to turn over 10,000 GPUs to a new customer with one day's notice, said Chen Goldberg, an executive vice president. CoreWeave's near-term capacity remains essentially sold out, CEO Mike Intrator said on the company's August earnings call.

Baby neoclouds

If companies want GPUs immediately, they might have to go beyond the name brands. Some neoclouds aren't household names because they target specific countries, which can be viable in certain cases.

"Capacity right now is tight, and your relationships with your suppliers is actually one of the most closely guarded secrets for companies like ours," said Zhen Lu, CEO of Runpod.

Specialist neoclouds can offer more flexibility than the larger GPU clouds, which often require upfront payments and long-term commitments. Some sell so-called bare-metal GPUs, which give customers more control but also require them to manage more of the technical work themselves.

Companies using these smaller neoclouds share the same concerns: When can they get the GPUs, and at what price? Sunny Smith, co-founder and technology chief at Massed Compute, said customers are often willing to commit to capacity when they expect prices to rise.

Bring your own

Oracle, one of the world's largest cloud providers, is letting clients bring in their own GPUs. The software maker has more debt than Amazon or Microsoft, and its credit rating is lower so it has less flexibility to go on a GPU spending spree. But it's happy to operate the technology.

"As we're generally able to preserve and improve margins in the case of things like bring-your-own-hardware, the ROIC for those types of structures will be even higher," Oracle CFO Hilary Maxson told analysts on a June earnings call, using the acronym for return on invested capital.

Oracle hasn't disclosed the names of companies that choose this path. John DiFucci, a Guggenheim Securities analyst who recommends buying Oracle shares, said it would make sense for Advanced Micro Devices and Nvidia, the top two producers of GPUs, to bring their own to Oracle.

Super Micro Computer CEO Charles Liang speaks at the Computex conference in Taipei, Taiwan, on June 5, 2024.

Annabelle Chih | Bloomberg | Getty Images

Instead of buying thousands of GPUs, early-stage startups with limited capital can borrow them for hours at a time for less through clouds. And for companies with heavy-duty computing needs, Oracle's new route might be more attractive than building whole data centers. OpenAI committed to spending over $300 billion with Oracle over five years, but it hasn't mentioned anything about bringing in GPUs.

The method might make sense for companies that have the capital to purchase AI chips but don't have enough power, data center space or skilled labor. Like its hyperscaler peers, Oracle works hard to secure all three in healthy quantities.

Tactical deals

Another emerging option is to cut large deals with firms that have truckloads of GPUs for rent.

SpaceX arranged to turn over excess capacity in separate deals with hyperscaler Google and open-source startup Reflection.

SpaceX builds a data center in Southaven, Mississippi, on Aug. 2, 2026.

In April, SpaceX agreed to provide Cursor with GPUs and then bought the AI coding startup outright for $60 billion. And in May, SpaceX landed a deal to rent GPUs to Anthropic for $1.25 billion each month through mid-2029. That's more than what most startups can afford.

For SpaceX, though, the numbers work out nicely.

"The current economics have translated into a less than one-year payback on our new capital deployments for compute," Bret Johnsen, the company's finance chief, told analysts in August.

It's not only SpaceX. In July CNBC reported that Meta was working to form a cloud unit that could sell AI computing power.

Going old school

Meanwhile, companies continue to install GPU-filled servers in on-premises data centers the old-school way as CEOs work to balance capability with cost control.

Revenue nearly doubled in the enterprise and small and medium business parts of hardware maker Lenovo's Infrastructure Solutions Group during the June quarter. "We're seeing more and more enterprises now starting to say, 'How do I bring AI into my four walls?'" said Vlad Rozanovich, a senior vice president.

People walk through the hallways at Equinix Data Center in Ashburn, Virginia, on May 9, 2024.

Amanda Andrade-Rhoades | The Washington Post | Getty Images

The hourly spot price for an Nvidia B200 GPU has more than doubled since March, according to data from Ornn, a startup that maintains indexes.

Collaboration software maker Dropbox relies on GPUs in its data centers, CEO Ashraf Alkarmi said.

"If we want to do a lot more, I think our supply chain connections will still be beneficial and a structural advantage," he said.

Everpure, which sells data center storage hardware and software, has acquired its own GPUs, to run open-weight AI models for the company's software engineers, said CEO Charlie Giancarlo.

"In a very dynamic pricing environment, it's always good to have multiple sources that you can go to," he said.

WATCH: Hightower's Stephanie Link: I like that Nvidia is showing growth and confidence

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Trump awards science medal to Elon Musk, Jensen Huang

Credited to DigiTimes$ · digitimes.com10h ago

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US President Donald Trump will award the National Medal of Science to Nvidia CEO Jensen Huang and several other tech leaders, with major figures from the industry set to gather again on October 8, local time. The honors highlight Trump's push to spotlight technology executives…

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Chips & Supply · United States

Upscale AI Partners With Nvidia While Competing With It

Credited to The Next Platform · nextplatform.com · 20h ago

Photograph · The Next Platform

Every CPU maker always had its own and very proprietary way to link multiple CPUs into shared memory clusters and for the most part, this remains true despite the efforts of the CCIX consortium and even the CXL consortium to create a kind of universal glue to make many CPUs look like one much bigger CPU.

Attempts to create CPU SMP and NUMA standards largely failed because as much as customers would have liked to have a standard way to link CPUs together, the odds that they had a workload that would mix and match CPUs of different architectures from different vendors together seemed small. Moreover, each vendor could innovate on their SMP and NUMA chipsets, which were eventually embedded in the CPUs themselves, at a pace that was distinct from advances in their core designs, memory subsystems, and I/O. And most importantly, they were not dependent on some standards body to approve features they needed and they could differentiate as they saw fit.

So why do we think there will be a standard version of what amounts to a NUMA interconnect – what is commonly called scale up networking these days – for GPUs and XPUs? Because hope springs eternal, and because markets love competition because it drives down costs. (Substitution is a law of economics, not just a good idea....)

What is clear is that Nvidia can – and does – charge a hefty premium for its NVSwitch and NVLink ports, which offer unparalleled bandwidth and decent latency to glue as many as 72 GPUs to each other as a shared memory system that the company calls the NVL72 rackscale machine. With its NVLink Fusion effort, Nvidia is allowing those making custom CPUs to add NVLink ports to their devices so they can hook into the same memory space as Nvidia GPUs, and is similarly allowing XPU accelerators made by hyperscalers, cloud builders, and AI model builders to have NVLink ports so they can be put inside Nvidia’s rackscale architecture. Thus far, as far as we know, Nvidia is not allowing customers to add NVLink ports to both their own CPUs and their own XPUs and just buy NVSwitches and a license to the NVLink protocol from Nvidia to make shared memory systems.

That day might come, though, as I talked about in August in In The Long Run, Nvidia NVSwitch Is The InfiniBand Of Scale Up AI Networks. And in the longest of runs, should the right technology come to the market, Nvidia could adopt an industry standard scale up network with its own protocol overlays – particularly when it comes to AI inference. (There is no reason for Nvidia to yield such ground for AI training, where it has the vast majority of the market locked up.)

This is why Upscale AI, which I talked to back in January as it was raising $200 million in Series A funding, is so interesting. (I am not going to go over the history of the company here, and the serious players involved, so read that story if you want the background.) Upscale AI has raised $500 million in total so far, and its valuation has doubled to $2 billion and its employee count has also more than doubled to over 300 people since that January announcement. This week, the top brass at Upscale AI raised the curtain a little bit more on its plans for scale up and scale out networking aimed specifically at AI clusters.

Before you get too excited, we did not get much in the way of speeds and feeds for the “SkyHammer” scale up switch ASIC that has been in development for several years. Rajiv Khemani, co-founder and executive chairman at Upscale AI, and Barun Kar, its chief executive officer, have long and deep experience in high performance systems and networking, so we expect some interesting twists.

What we do know from reading that slide from a webinar – that’s Arvind Srikumar, senior vice president of product and marketing at the company, giving that part of the presentation – is that SkyHammer will have an aggregate bandwidth of 115.2 Tb/sec, which is state of the art and stands toe to toe with the best from Cisco Systems, Broadcom, and Nvidia for their scale out network ASICs. The chart also says that the SkyHammer chips and the SkyFabriX architecture it supports will eventually have switch gear that spans multiple petabits per second of aggregate bandwidth.

This will very likely be accomplished by ganging up multiple SkyHammer ASICs inside of a switch, much as Broadcom and Cisco often do for both fixed port and modular switches. As you know from watching the Broadcom and Meta Platforms presentations over the past decade in The Next Platform, you can double the bandwidth of ports with any given switch ASIC architecture by creating a baby two-tier network from six independent ASICs. Half the bandwidth of four of the ASICs are used to create downlinks, and the other half are used to cross-couple the four ASICs together using a pair of the same chips. This obviously doubles the latency for many of the port hops through the switch, but not for all of them. You can also use specialized fabric connectors (like the “Ramon” devices that are paired with Broadcom’s “Jericho” deep buffer ASICs) to create even larger modular switches.

Upscale AI will no doubt also create devices with higher aggregate bandwidth, and that is because the economics works out. You can build a switch with 2X the native bandwidth per port out of six switch ASICs, but it costs around 1.5X per port to do it compared to making a single device with 2X the bandwidth. (That is street pricing, not the cost of making the ASICs.) The company did not provide a roadmap for future SkyHammer ASICs.

As far as memory domain scalability goes, the SkyHammer chip’s architecture can support up to 576 accelerators in a single networking tier, and just like NVSwitch, using multiple network tiers it can scale further – in this case, to “thousands of accelerators.” How many thousands remains to be seen, but 1,024 or 1,156 seem to be the two numbers to aim for. The UALink consortium is promising 1,024 XPUs in a single tier, and Nvidia is going to try to scale NVSwitch beyond its current 576 GPU limit to 1,152 GPUs in the “Rubin Ultra” GPU generation. (This is with a multi-tier network, not a single tier.) Nvidia supports 72 GPUs in a single domain now, with 576 GPUs being possible with multiple tiers but not intended for production workloads.

If you do the math on that 576 devices number for SkyHammer, each device is getting 200 Gb/sec of bandwidth. This is obviously a lot less than the 1.8 TB/sec that an NVSwitch 5/NVLink 5 generation offers on the “Grace-Blackwell” NVL72 systems. The “Vera-Rubin” NVL72 systems will have NVSwitch 6/NVLink 6 with 3.6 TB/sec. With 72 XPUs, SkyHammer is delivering 1.6 Tb/sec per port, which is still only 200 GB/sec of memory bandwidth over the scale up network. That is one-sixth that of the NVSwitch 5 stack. (I have normalized the NVSwitch names

It is not at all clear that bandwidth matters more than latency, particularly for AI inference workloads. And predictable latency is always more important than low latency, as Google has taught us.

“One of the most important metrics when it comes to tokens is latency,” Srikumar tells The Next Platform. “We have to produce, but this is where it's a little bit different from what Rajiv and Barun has done in the past. With AI, the latency has to be super predictable, and we have built into the ASIC predictable jitter. And at the same time, transmission reliability within the domain is also important. We have some protocols which are standards-based that provide link level reliability, but within the fabric we have a mechanism to ensure the fabric is 100 percent lossless. This is why we call it reliable and predictable latency, where we are minimizing the latency. And you might be surprised with the numbers once we announce the specifics, If you look at a lot of Ethernet fabrics, there are pretty much a fixed pipeline arrangement, and it's a pipeline arrangement with less parallelism. Here we are doing a lot of things that are pushing the envelope on what has been done in the Ethernet fabrics to date. We believe SkyFabriX is going to be uniquely positioned in the market because we started with a clean sheet of paper rather than taking an Ethernet fabric and retrofitting the protocols that are needed for scale up.”

No matter what anyone believes, we are about to find out what works and what does not as people begin putting the UALink over Ethernet (UALoE in the common parlance today) and the ESUN memory protocols on top of fast Ethernet switches that have been tweaked to do scale up.

It is interesting to note that SkyHammer is running UALoE, not UALink proper, and the fact that it is running ESUN shows you that under the covers SkyHammer is an Ethernet switch ASIC. I was hoping that it was going to be a faster switch ASIC with more radix that could run UALink native and run an emulated version of ESUN when necessary. But, so it goes.

Here is the neat bit. Upscale AI has partnered with Nvidia to be its supplier of choice for scale out fabrics, and is choosing the Spectrum-X Ethernet switches in particular. Obviously not the Quantum-X InfiniBand switches, since the company’s founders are big believers in Ethernet eventually conquering InfiniBand. Some would say with the first generation of Ethernet switches that are compatible with the Ultra Ethernet Consortium’s specifications, this goal will be accomplished after nearly three decades of InfiniBand setting the high performance standard. InfiniBand will still beat the crap out of Ethernet when it comes to port-to-port hop latency. But Ethernet can get closer, and can scale out a lot further than InfiniBand for a given network topology. And Nvidia will be selling InfiniBand like crazy for those who need the lowest latency for their HPC and AI workloads. Fear not.

To be specific, Upscale AI will be acquiring Nvidia’s Spectrum-X ASICs and building its own scale-out switches from them as well as porting its SkyOS network operating system that runs on the scale up SkyFabriX switches so it also runs on the Spectrum-X ASICs. The reason this is possible is that SkyOS is an AI-optimized version of the SONiC network operating system and its related Switch Abstraction Interface (SAI) created by Microsoft way back in 2016 and which has become a de facto standard in many hyperscaler and cloud builder datacenters. The Upscale AI versions of Spectrum-X will support 400 Gb/sec, 800 Gb/sec, and 1.6 Tb/sec ports.

The important thing, according to Upscale AI, is that SkyOS will span both scale up and scale out networks, and its SkyCMD telemetry and orchestration tool will see both networks as a whole. The other important thing is that customers will be able to use any CPU or any GPU or any XPU front-ended by a DPU or NIC and will be able to mix and match these devices to create AI clusters. That is all well and good except that Nvidia is not supporting SkyHammer and its SkyOS as an alternative to NVSwitch for scale up. There is no way – as yet – to do scale up with Nvidia GPUs. AMD is actually counting on companies like Upscale AI to provide scale up networking options supporting UALink, UALoE, or ESUN, and it may lose patience and just acquire Upscale AI to own one of the options and make money from it. ( Why not? AMD is rich in stock funny money, and it may as well spend some of it.)

One last thing: Nvidia can always cut back on the bandwidth per port in an NVSwitch ASIC and increase the radix of the switch so it can flatten its NVSwitch network. But it will have to sacrifice bandwidth to do that. It might be faster to port the NVLink protocol to SkyHammer.... and have Nvidia become the dominant supplier of UALoE or ESUN switchery. We shall see.