Enterprises can quickly set up managed Slurm environments with automated resiliency and cost optimization through the Dynamic Workload Scheduler. The platform also includes hyperparameter tuning, data optimization, and built-in recipes with frameworks like NVIDIA NeMo to streamline model development.
Enterprises weigh AI training gains
Building and scaling generative AI models demands enormous resources, and for many enterprises, the process can be slow and complex.
In its post, Google pointed out that developers often spend more time managing infrastructure, including handling job queues, provisioning clusters, and resolving dependencies, than on actual model innovation.
Analysts suggest that the expanded Vertex AI Training could reshape how enterprises approach large-scale model development.
“Google’s new Vertex AI Training strengthens its position in the enterprise AI infrastructure race,” said Tulika Sheel, senior VP at Kadence International. “By offering managed large-scale training with tools like Slurm, Google is bridging the gap between hyperscale clouds and specialized GPU providers like CoreWeave or Lambda. It gives enterprises a more integrated, compliant, and Google-native option for high-performance AI workloads, which could intensify competition across the cloud ecosystem.”
Others pointed out that Google’s decision to embed managed Slurm directly within Vertex AI Training reflects more than a product update. It represents a shift in how Google is positioning its cloud stack for enterprise-scale AI.
