The Futurum Group

Developing advanced AI models is only one part of the challenge. Organizations also need the infrastructure and processes to train, deploy and run those models efficiently. Zyphra and AMD explore how modern AI architectures and scalable computing environments support the full AI lifecycle. The discussion covers frontier-scale model development, inference infrastructure powered by AMD Instinct GPUs and the growing importance of heterogeneous compute for modern AI workloads. Watch the on-demand conversation to explore how organizations can improve efficiency and reliability from model development through deployment.

As AI models become more complex, the infrastructure supporting them must deliver consistent performance, scalability and reliability. Zyphra and AMD discuss how modern AI infrastructure can meet these demands through AMD Instinct GPUs, open-source software and fault-tolerant training environments. The discussion examines the technical considerations behind large-scale AI training and how organizations can improve infrastructure efficiency while supporting demanding workloads. Explore these challenges and the technologies helping address them in the on-demand conversation with Zyphra and AMD.

AI efficiency depends on more than hardware performance. Model architecture, infrastructure design and engineering processes all influence how effectively AI systems operate. Zyphra and AMD examine how these elements work together to support advanced model development and more efficient AI operations. From architectural decisions to AI-assisted engineering workflows, the conversation explores practical approaches to improving performance, managing complexity and supporting reliable AI development. Watch the full discussion to learn how a more comprehensive approach to AI efficiency can benefit enterprise workloads.

AI agents are creating new opportunities to improve efficiency in model development and infrastructure operations. By supporting repetitive engineering tasks such as running experiments, writing unit tests and managing training clusters, AI agents can help teams focus more resources on research and innovation. Zyphra and AMD explore the role of AI agents in engineering workflows alongside the infrastructure and human expertise needed to build advanced AI systems. Watch the on-demand discussion to discover how AI agents and modern infrastructure can work together to support more efficient AI development.

Scaling AI requires more than powerful computing infrastructure. It demands efficient model architectures, reliable training environments and the ability to support complex workloads. Zyphra and AMD explore how enterprises can improve AI efficiency throughout the entire lifecycle, from model design and training to inference and deployment. The conversation examines how advances in infrastructure, open-source technologies and engineering workflows can help organizations build more efficient and reliable AI systems. Watch the on-demand discussion to gain insights into the technologies and approaches driving AI efficiency at scale.

Enterprise AI adoption is moving, but not every organization is keeping the same pace. ETR found that 40% of organizations surveyed are one or more generations behind or have deliberately frozen on a model version. Only 16% adopt new capabilities within weeks of release. Another 44% typically adopt within one to two quarters. See how enterprise adoption is tracking against the pace of frontier AI.👇