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Microsoft will deploy AMD’s Helios rack-scale AI accelerator ‘at scale’ on Azure – Radeon Instinct MI455X and Epyc Venice power will be available through Redmond’s cloud infrastructure
The demand for AI compute is already insatiable, and it seems only poised to grow in the wake of the introduction of frontier-class open models like Kimi K3 that anybody can potentially fine-tune and serve. Against this backdrop, Microsoft and AMD are teaming up to get Redmond more AI FLOPS for both internal and external use. The two companies announced this morning that Microsoft will commit to adding AMD's Helios rack-scale AI accelerator in volume to run frontier-model workloads in its own data centers, as well as for Azure AI infrastructure customers and services.
The partnership makes next-gen AMD AI compute available to Azure customers like AI labs for AI training and inference serving workloads, and it’ll also underpin managed compute for enterprise customers looking to deploy AI workloads through Microsoft Foundry.
The two companies didn't indicate the exact size of Microsoft's Helios deployment in either watts or dollars, but the commitment would seem to be another major win for AMD as it seeks to grab data center GPU share from Nvidia. AMD has struck massive partnerships with OpenAI and Meta in the past year with gigawatts of compute installations and hundreds of billions of dollars potentially hanging in the balance.
For a quick refresher, the Helios rack-scale accelerator will take the fight to Nvidia’s Vera Rubin NVL72 system when it arrives later this year. Helios joins together 72 next-generation Instinct MI455X GPUs with an aggregate of 31.1TB of HBM4 memory capacity across the system. Those GPUs offer as much as 1.4 exaFLOPS of FP8 compute and 2.9 exaFLOPS of FP4 for AI models using those OCP AI data types.
AMD is targeting 260 TB/s of scale-up bandwidth within the rack, on par with Nvidia’s Vera Rubin NVL72 rack-scale system, and 43 TB/s of scale-out bandwidth using UALink over Ethernet, or about twice that of Vera Rubin, although the performance of UALink over Ethernet in practice remains to be seen.
Microsoft and AMD also announced that Azure will add two new VM series built on AMD's upcoming sixth-gen Epyc Venice CPUs: the HDv2 series for "agentic AI and data pipelines," and the HXv2 for semiconductor design workflows. Microsoft will also leverage its existing deployment of AMD Pensando DPUs to integrate that hardware into its Azure Boost offerings to accelerate networking and storage processing operations.
Tom’s Hardware will be on the ground at AMD’s Advancing AI event this week, where we expect to learn more about AMD’s AI ambitions for the second half of this year and beyond. Stay tuned for our coverage from that event.
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As the Senior Analyst, Graphics at Tom's Hardware, Jeff Kampman covers everything that has to do with graphics cards, gaming performance, and more. From integrated graphics processors to discrete graphics cards to the hyperscale installations powering our AI future, if it's got a GPU in it, Jeff is on it.
Nvidia profits have undoubtedly lit a fire under AMD like never before. The pie has grown so massive everyone wants a bite out of that pie. The competition usually makes both better.
AMD has done very well and has probably been slowly gaining on Nvidia recently and with their upcoming products. If they can continue to improve on the developer, software, and interconnect areas they should provide some solid competition.
MS: Let's deploy at scale!
Power company: Sorry mate, you are maxing out the grid.
MS: Let's just deploy them in our data centers first then.
Building company: Sorry mate, your data center is not ready either.
Big tech likes to give the impression that they are doing MASSIVE investments to continue milking the AI hype. But their actions speaks otherwise and the physical limitations are conveniently ignored.
MS: Let's deploy at scale!
Power company: Sorry mate, you are maxing out the grid.
MS: Let's just deploy them in our data centers first then.
Building company: Sorry mate, your data center is not ready either.
Big tech likes to give the impression that they are doing MASSIVE investments to continue milking the AI hype. But their actions speaks otherwise and the physical limitations are conveniently ignored.
They might not get deployed but the GPUs are getting bought. They then pay Nvidia/AMD to store them until they can deploy. They’re doing that because they don’t want to spend the money on the construction unless they know for a 100% fact they have already the compute to deploy into it.
