AT&T and Microsoft scale trillion-token workloads with Microsoft Foundry and AMD

First-of-its-kind telecom AI deployment Telecommunications organizations are increasingly looking to AI to help teams navigate highly specialized domains, but generic models often lack the industry-specific knowledge needed to understand telecom networks, standards, and operations. To address that gap, AT&T created their Open Telco (OTel) models, the next generation of telecom-focused AI designed to bring deeper telecommunications expertise into AI systems. Building OTel2.0 required more than training a large language model, it reflected a broader issue many organizations face: how to build domain-specific AI systems at scale while balancing cost, performance, and operational complexity. Cost management quickly became a key consideration. To continue advancing telecom-focused AI, AT&T needed a platform capable of supporting OTel2.0 development at an entirely new scale. Where teams previously had to own and manage deployments, infrastructure, and the associated operational overhead, Foundry Managed Compute provided a more streamlined way to access dedicated graphics processing unit (GPU) capacity. This transformation requires more than powerful models; it requires the ability to scale without compromising cost, flexibility, or performance. Learn how OTel2.0 scales telecom AI Using Microsoft Foundry Managed Compute, AT&T was able to experiment across multiple open models, optimize workloads across different GPU architectures, and process massive volumes of telecom data all within a unified platform. The result was an AI development environment capable of supporting trillions of tokens while giving teams the flexibility to iterate, optimize, and innovate faster. Model choice meets infrastructure flexibility Building OTel2.0 required flexibility across both models and infrastructure. Rather than standardizing on a single model, AT&T adopted a multi open-model strategy. Open models were central to AT&T’s approach because they provided the flexibility to work with approved telecom data, tailor the workflow for domain-specific model development, and support large-scale experimentation with greater control over cost and deployment strategy. Through Microsoft Foundry, the team deployed several models from the Hugging Face collection, including Phi-4, OSS-120B, and Gemma-4, to support different stages of development, from synthetic data generation and data preparation to reasoning-intensive workloads and broader model development efforts. Phi-4 played a significant role in this process, processing more than 700 billion tokens a month as part of the broader data preparation and training workflow for OTel2.0. Every company in the world needs to build its own AI, and that is only possible with open models and open source. AT&T is championing this vision, building on open models like Phi-4 and Gemma, and giving OTel back to the community as a telecom AI foundation others can build upon. Microsoft Foundry makes this practical at scale, bringing the latest open models from the Hugging Face collection together with AMD and NVIDIA GPUs in one place, so teams can pick the right model and the right hardware, then deploy in hours instead of weeks. —Jeff Boudier, Vice President of Product, Hugging Face Developing OTel2.0 also required infrastructure capable of operating at telecom scale. AT&T used approximately 530 GPUs through Microsoft Foundry Managed Compute spanning multiple GPU architectures including 430 AMD Instinct™ MI300X GPUs. This heterogenous approach gave AT&T more flexibility in how models were deployed and optimized as requirements evolved. ModelExample workloadPhi-4Around 700B tokens a month for data preparation and synthetic data generationOSS 120BHigher-reasoning workloads Gemma 4OTel2.0 development workflowsTable 1: Explains what open source models were used and how This flexibility illustrates a broader trend across AI development. Organizations increasingly need platforms that allow them to