In recent years, AI workloads have grown exponentially—not only in the deployment of large language models (LLMs) but also in the demand to process ever more…
In recent years, AI workloads have grown exponentially—not only in the deployment of large language models (LLMs) but also in the demand to process ever more tokens during pretraining and post-training. As organizations scale up compute infrastructure to train and deploy multi-billion-parameter foundation models, the ability to sustain higher token throughput has become mission critical.
