The useful question is what changes for users, developers or buyers, and whether the announcement stays industry context or becomes something people can actually use.

AT&T developed OTel2.0, a telecom-specific AI model, to address gaps in generic AI systems that lack industry expertise. The project required scalable infrastructure to handle massive data volumes while balancing cost and performance. Microsoft Foundry Managed Compute provided dedicated GPU capacity, eliminating the need for AT&T to manage deployments and infrastructure overhead, streamlining development processes.

AT&T adopted a multi open-model strategy, deploying models like Phi-4, OSS-120B, and Gemma-4 from Hugging Face via Microsoft Foundry. Phi-4 processed over 700 billion tokens monthly for data preparation and training. The approach allowed AT&T to tailor workflows for telecom-specific needs, control costs, and accelerate innovation while maintaining flexibility in model selection and deployment.

The project utilized approximately 530 GPUs, including 430 AMD Instinct MI300X GPUs, across heterogeneous architectures to optimize model deployment and performance. Microsoft Foundry enabled rapid deployment in days rather than weeks, supporting AT&T’s need for speed and scalability. This infrastructure flexibility aligns with broader industry trends, where organizations prioritize model choice, cost optimization, and operational efficiency in AI development.

AT&T processed around 1 trillion tokens for OTel2.0, combining raw documents from GSMA with synthetic data generated using open models like Phi-4. The approach saved tens of millions of dollars compared to using frontier models, allowing teams to focus on larger-scale experimentation and business value. Microsoft Foundry’s managed compute platform proved critical in transforming infrastructure from a deployment consideration into a strategic component of AI development.