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While data centers take on increasingly compute-intensive workloads such as AI and ML training, HPC, and large-scale analytics, traditional server and system designs start to fall short. This episode explores what it means to design high-density compute systems built around CPUs, GPUs, memory, storage, and high-speed networking, where data movement, interconnect bandwidth, accelerator utilization, and reliability become the defining constraints. It examines the hardware and system-level choices that improve throughput and latency, as well as the decisions that quietly degrade performance at scale. Join this episode to learn:

• Hidden pitfalls in data movement, scheduling, and workload placement
• Patterns that improve throughput, latency, GPU and accelerator utilization
• Why scale-out strategies fall short at high density
• Practical guidelines for assessing readiness and planning next-generation infrastructure