Google Cloud offers managed Slurm to rival CoreWeave and AWS in AI training
When Google Cloud announced a managed Slurm service this week, it felt like a direct answer to the AI-heavy workloads many teams are wrestling with. By wrapping the open-source scheduler in a fully supported, cloud-native package, they’re clearly trying to pull in the same folks who have been using niche players such as CoreWeave or the massive AWS GPU fleet. The idea is to move away from the patch-work, on-prem clusters we’ve all built before and toward an environment that can grow as quickly as the training jobs demand.
For groups that usually spend weeks - sometimes months - ordering and installing hardware, a ready-to-go option could shave off a lot of time and the day-to-day hassle. Still, it’s unclear whether the service will satisfy both camps: the companies that only need to fine-tune an existing model and the ones that want to start a model from scratch. If it works, we might see a shift in how pricey, compute-heavy development stages are handled.
Some firms are happy just tweaking large models, but a handful plan to build their own from the ground up - a task that needs solid GPU access. Google Cloud seems poised to step deeper into that workflow with its new Vertex AI Training service.
Some enterprises are best served by fine-tuning large models to their needs, but a number of companies plan to build their own models, a project that would require access to GPUs. Google Cloud wants to play a bigger role in enterprises' model-making journey with its new service, Vertex AI Training. The service gives enterprises looking to train their own models access to a managed Slurm environment, data science tooling and any chips capable of large-scale model training. With this new service, Google Cloud hopes to turn more enterprises away from other providers and encourage the building of more company-specific AI models.
Will enterprises move to Google Cloud for their own model builds? The new Vertex AI Training service bundles a managed Slurm environment with data-science tools and support for any GPU-class chip. That puts Google in the same lane as CoreWeave and AWS, which already chase large-scale AI workloads.
For firms that just want to fine-tune existing models, the offering might feel like overkill; for those planning to build from scratch, on-premise GPU clusters are still an option. Google’s bet seems to rest on how smoothly customers can shift existing pipelines into the managed Slurm stack. The announcement talks about enterprise-scale training, but pricing, performance guarantees, and integration depth are still vague.
So adoption could be slow, especially for companies nervous about vendor lock-in. The service does broaden Google’s AI portfolio, yet it’s unclear whether it will pull the same crowd as its rivals. As the market for custom model development expands, the managed Slurm option adds another path - its real impact will likely hinge on how it performs in everyday use.
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Common Questions Answered
What is the managed Slurm offering introduced by Google Cloud and how does it differ from traditional on‑premise clusters?
Google Cloud’s managed Slurm packages the open‑source scheduler into a fully supported, cloud‑native service that can scale on demand. Unlike ad‑hoc on‑premise GPU clusters, it eliminates the need for hardware procurement and maintenance, providing enterprise‑grade training environments directly in the cloud.
Which existing cloud providers does Google Cloud aim to compete with through its Vertex AI Training service?
The service positions Google alongside niche provider CoreWeave and the broader AWS GPU fleet, both of which already attract large‑scale AI workloads. By offering a managed Slurm environment, Google Cloud seeks to draw the same customers that currently favor those platforms.
How does the new Vertex AI Training service support enterprises that want to build their own AI models?
Vertex AI Training gives enterprises access to a managed Slurm environment, integrated data‑science tooling, and support for any GPU‑class chip suitable for large‑scale model training. This combination enables companies to fine‑tune or build models from scratch without managing their own hardware infrastructure.
What types of hardware are supported by Google Cloud’s managed Slurm environment?
The managed Slurm service supports any GPU‑class chip capable of large‑scale model training, allowing flexibility across different accelerator vendors. This includes the latest NVIDIA GPUs as well as other compatible accelerator hardware offered through Google Cloud.
For which AI workloads might the managed Slurm service be considered excessive?
Enterprises that primarily fine‑tune existing models rather than train large models from the ground up may find the managed Slurm offering unnecessary. In such cases, simpler, less resource‑intensive solutions could be more cost‑effective than a full‑scale Slurm environment.