Scalability of AI workloads is a primary challenge when integrating AI into existing IT infrastructure. AI tasks, especially training and inference on NVIDIA GPUs, demand significant compute, memory, and networking resources, which legacy systems may not handle efficiently. Scaling these workloads across clusters or hybrid environments requires careful planning, as noted in NVIDIA's "AI Infrastructure and Operations Fundamentals" and "AI Adoption Guide." User-friendly interfaces (A) are secondary to technical integration. Hardware compatibility (C) is less challenging with NVIDIA's broad support. Cloud provider selection (D) is a decision, not a core challenge.
NVIDIA identifies scalability as a key integration hurdle.