Microsoft Azure AI gets a major AMD infrastructure boost

AMD and Microsoft are expanding their partnership across GPUs, CPUs, networking and software, with Microsoft Azure AI set to gain a new layer of AMD-powered infrastructure for large-scale AI workloads.

At the centre of the move is Microsoft's planned deployment of the AMD Helios Rackscale Solution. The platform is designed to support frontier model AI inference for Microsoft, its AI customers and Azure AI services. AMD said it will begin shipping Helios to customers, including Microsoft, in the second half of 2026.

The announcement points to a broader shift in how cloud infrastructure is being built for AI. Rather than focusing only on compute, the collaboration brings together processing, networking and software as part of a single infrastructure approach.

Microsoft Azure AMD Helios deployment expands

AMD Helios combines AMD Instinct MI455X GPUs, AMD EPYC "Venice" CPUs, Pensando networking and ROCm software in an integrated rack-scale platform.

On Azure, the system will be used for inference workloads covering frontier models, Azure AI services and customer applications. This gives model builders access to AMD-powered infrastructure for training and serving large-scale AI models, while enterprise customers can use Azure Foundry Managed Compute to deploy and scale production AI workloads.

The Microsoft Azure AMD Helios deployment also signals how rack-scale infrastructure is becoming central to supporting increasingly demanding AI workloads.

Azure AI infrastructure expansion gains pace

The partnership is also extending into Azure's virtual machine portfolio. Two new VM series, Azure HDv2 and Azure HXv2, will be powered by 6th Gen AMD EPYC Venice chips.

HDv2 is designed for agentic AI and data pipelines, while HXv2 targets semiconductor design. Together, the new VM series broadens the role of AMD EPYC processors across AI, data and engineering workloads. This expansion gives Azure customers more AMD-based options beyond dedicated AI infrastructure, covering a wider set of workloads that sit around AI development and deployment.

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