You can now ask questions directly within our broadcasts using our new AskAI feature.

Capital markets companies have long relied on large-scale, ultra-low latency HPC infrastructure to power marketing, quantitative trading, research, and real-time decision support applications. Today, the rapid adoption of AI, and increasingly agentic AI, is fundamentally changing the performance, networking, storage, and power demands placed on trading environments and the data centers that support them. As companies race to operationalize AI-driven analytics, automation, and decision-making while maintaining deterministic performance and regulatory compliance, infrastructure strategies must evolve quickly to support new workloads at scale. Join this episode to explore how capital markets companies are redesigning hardware, software, and data center architectures to meet the next generation of trading and AI infrastructure requirements. Key discussion points include:

• How AI is reshaping infrastructure requirements across capital markets
• The evolution of HPC, GPU, networking, and storage architectures for latency-sensitive trading environments
• The rise of mid-frequency trading strategies that balance latency with more intelligent results
• Capital markets AI inference benchmark results demonstrating the value of co-optimized hardware and software stacks
• Future-ready data center strategies supporting AI-driven trading workloads