Abstract
Workloads using generative artificial intelligence trained on large language models are frequently throttled by insufficient resources (e.g., memory, storage, compute, or network dataflow bottlenecks). If not identified and addressed, these dataflow bottlenecks can constrain Gen AI application performance well below optimal levels.