Uploaded August 2026 | Updated September 2026, 4 days ago
Everyone wants to scale AI. But scaling AI also means scaling the infrastructure behind it.
As organizations move from experimentation to production and particularly as agentic AI takes off, the demands on computing power, energy, cost and data center capacity are growing rapidly.
The key lesson? There is no one-size-fits-all approach.
The smartest organizations are starting with the outcome they want to achieve, understanding the workloads involved, and then choosing the right mix of computing resources to support them.
I recently spoke with Alexander Troshin from AMD about why scalability, performance and energy efficiency are becoming so important as businesses build the infrastructure needed for the next phase of AI.
Learn more about AMD EPYC™ processors here:
amd.com/en/resources/epyc-tools.html?utm_medium=social&utm_source=youtube&utm_campaign=q126_077pi&utm_content=bernardmarr
#Sponsored #AMDPartnership #AI #ArtificialIntelligence #AgenticAI #AIInfrastructure
Everyone wants to scale AI. But scaling AI also means scaling the infrastructure behind it.
As organizations move from experimentation to production and particularly as agentic AI takes off, the demands on computing power, energy, cost and data center capacity are growing rapidly.
The key lesson? There is no one-size-fits-all approach.
The smartest organizations are starting with the outcome they want to achieve, understanding the workloads involved, and then choosing the right mix of computing resources to support them.
I recently spoke with Alexander Troshin from AMD about why scalability, performance and energy efficiency are becoming so important as businesses build the infrastructure needed for the next phase of AI.
Learn more about AMD EPYC™ processors here:
amd.com/en/resources/epyc-tools.html?utm_medium=social&utm_source=youtube&utm_campaign=q126_077pi&utm_content=bernardmarr
#Sponsored #AMDPartnership #AI #ArtificialIntelligence #AgenticAI #AIInfrastructure
