Your AI Journey, Accelerated: How Enterprise Teams Are Scaling AI From Concept to Impact

Enterprise AI is no longer a question of “if”—it’s about how fast and how well you can scale. But getting from early experimentation to production-grade infrastructure remains a challenge for many IT leaders. 

At the AMD Advancing AI 2025 event, global innovators including Dell Technologies, Supermicro, Vultr, TensorWave, and AWS shared how they’re helping organizations move through each critical phase of AI adoption with open, flexible, and solutions using AMD technology. 

Each milestone on the AI journey requires deliberate infrastructure choices. Here’s how forward-looking teams are getting it right. 

Getting AI Into Production Faster

The first hurdle for many IT teams is simply getting AI out of the lab and into the real world—without long development cycles or heavy cloud dependencies. 

To accelerate that journey, organizations are adopting modular, pre-integrated solution stacks that remove friction from deployment. Dell Technologies, in partnership with AMD, offers just that with its AI Factory framework. Powered by AMD NPUs, these systems support private distributed inferencing and on-device model execution. By integrating open tools like AMD ROCm™ software and Dell Omnia, Dell and AMD give IT teams the agility to manage AI workflows across environments—while keeping sensitive data confidential. 

This approach enables faster launches, tighter control, and a more flexible path to scale. 

Scaling Infrastructure for Growing Demands

Once AI is in production, the next challenge is building infrastructure that can scale with it—without constant reinvention. 

Supermicro and AMD deliver rack-scale, full-stack AI systems that are ready for enterprise and cloud deployment. Featuring AMD Instinct™ MI300X and MI350X GPUs, these platforms are engineered with advanced cooling and validated networking to support high-density compute environments. With improved efficiency, teams are meeting performance goals while keeping sustainability and energy efficiency in check. This kind of infrastructure ensures that scaling up doesn’t mean starting over. 

Managing Cost and Flexibility at Scale

As AI workloads expand, keeping costs predictable—and avoiding lock-in—becomes critical for long-term viability. 

Enterprises are meeting that challenge with open ecosystem solutions powered by AMD technology. Vultr, for example, delivers global infrastructure across 32 data centers. Meanwhile, TensorWave has launched a MI325X GPU-based liquid-cooled cluster, tailor-made for high-throughput training workloads like video generation. 

These shifts are giving IT leaders more budget headroom, more hardware control, and a broader range of deployment choices. 

Optimizing Cloud AI for Real-Time Performance

Cloud-native AI workloads, like real-time personalization or content delivery, demand not just power, but consistent, scalable performance under pressure. 

AWS, in partnership with AMD, now offers more than 100 EC2 instances powered by AMD EPYC™ CPUs, including the “Genoa”-based M7 series. 

These instances deliver performance advantages that are helping teams run real-time AI at scale with enhanced efficiency and less overhead. 

Advance With Confidence: AMD and its Ecosystem Are with You Every Step

Each milestone in the AI journey comes with new challenges—but also new opportunities for impact. What the Advancing AI 2025 event made clear is this: you don’t have to navigate the AI maturity curve alone. 

AMD and its robust and trusted ecosystem of partners, is enabling IT teams to: 

• Deploy faster 
• Scale smarter 
• Reduce cost and overhead 
• Optimize for performance
• And stay flexible as demands evolve 

Check out the full catalog of on demand partner and customer sessions from Advancing AI 2025 to see how enterprise teams are navigating each stage of the AI journey using AMD technology as their infrastructure foundation.