
AT&T, Dell, and AMD have launched OTel 2.0, the largest open-source telecom AI model.
It has been downloaded over 5 million times.
The model is built for production networks, not just research.
What happened
AT&T, Dell Technologies, and AMD have announced OTel 2.0, the largest and best-performing open-source model built for telecoms. Since release, it has been downloaded more than 5 million times, building on OTel 1.0 models that were downloaded nearly 30 million times.
Why it matters
OTel 2.0 is designed for telecom networks, which are vast, highly regulated, and unforgiving of errors. Unlike general-purpose models, it understands telco language, 3GPP standards, and modern network complexity from day one, moving open telco AI from models to production-ready deployment.
What to watch
Training will continue on Dell PowerEdge XE9785 servers with eight AMD Instinct MI355X GPUs in the upcoming weeks, and updated model weights will be released accordingly. AT&T processed more than 1 trillion tokens to generate roughly 440 billion training tokens.
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The release of OTel 2.0 marks a significant step in the Open Telco AI initiative, which was launched earlier this year with GSMA's help to create a shared ecosystem of telecom-specific data, standards, and benchmarks. AT&T joined as a founding contributor, bringing real operator insight based on genuine network conditions. The collaboration with Dell and AMD addresses the immense computing demands of training at this scale, with AMD providing the high-performance hardware foundation and Dell integrating it with AI-optimized servers for telecom environments.
A key aspect of this deployment is that it is on-premises, keeping data under operator control throughout training with no public cloud dependency. This approach addresses governance concerns in a highly regulated industry. As training continues in the coming weeks, updated model weights will be released, and the partnership aims to close the gap between what Open Telco AI can do and what it delivers in live production environments, where reliability is non-negotiable. The open nature of the models gives operators flexibility to deploy across clouds or on-premises without vendor lock-in.
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