Gcore Everywhere AI evolves to full-lifecycle management with Slurm, Jupyter, and token-based inference integrations

AI adoption has a fragmentation problem. Organizations routinely stitch together separate tools for development, training, and serving, each with its own infrastructure, access controls, and operational overhead. The result is a patchwork that slows teams down precisely when they need to move fast.

We built Everywhere AI to fix that. And today, we're taking the next major step to simplify these complexities.

Gcore Everywhere AI is now a full-lifecycle AI solution. With the addition of native integrations for managed Slurm orchestration, Jupyter notebooks, and token-based inference, we are providing a single execution layer for the entire AI journey, from the first line of code to global production scale.

Why we evolved Everywhere AI to a full-workload solution

One year ago, at KubeCon 2025, Gcore Everywhere AI was featured in the KubeCon keynote as a 3-click inference layer for on-prem, cloud, and private environments. It was designed to solve a specific, painful problem: the complexity of deploying and scaling models across diverse environments.

But we knew that for enterprises to truly scale, they needed more than just a destination for their models; they needed a home for the entire development process.

Over the past twelve months, we have steadily expanded the solution into a mature, structured AI execution platform.

The 2026 evolution reflects the complete operational realities of AI teams. By moving beyond inference to include development and training, we’ve eliminated the need for organizations to stitch together separate, incompatible tools. Today, Everywhere AI is a unified managed layer where you can standardize your entire AI stack on a single, Kubernetes-based architecture.

Enterprise AI adoption requires more than raw infrastructure; it demands intelligent orchestration and optimized execution. Everywhere AI has evolved into a unified platform that brings together development workflows, AI applications, and production inference within a Kubernetes-native architecture.

Seva Vayner, Product Director of AI and Cloud at Gcore

JupyterLab to bridge development and production

One of the biggest friction points in AI is the handoff between data scientists and infrastructure teams. Prototypes built in isolated local environments often require significant rework before they can be scaled or deployed.

By integrating JupyterLab directly into Everywhere AI, we’re bridging that gap. Developers can now experiment and prototype within the same environment that supports distributed training and production inference. This "build-where-you-deploy" approach reduces friction, ensures environmental consistency, and significantly shortens the path from proof of concept to production.

Managed Slurm for production-grade training

While Kubernetes is the gold standard for orchestration, heavy-duty distributed AI training often requires the specialized scheduling and multi-node coordination power of Slurm.

We’ve integrated Slurm as a managed capability within Everywhere AI to give teams the best of both worlds. You get HPC-grade GPU allocation and multi-node efficiency without the operational burden of building or maintaining the underlying infrastructure. For organizations training at scale, this reduces administrative load by up to 80% and accelerates development.

Tokens and Managed NVIDIA Dynamo for inference efficiency at scale

As AI applications move into production, the conversation shifts from "how do we build it?" to "how do we scale it affordably?"

Traditional infrastructure often relies on fixed GPU reservations, which can lead to overprovisioning and wasted spend. To solve this, Gcore is introducing token-based inference usage in addition to the existing endpoint usage option. Now, you can consume capacity based on actual output, paying only for the tokens consumed.

We’ve also integrated managed NVIDIA Dynamo as a managed capability. As we recently explored, Dynamo reimagines GPU scheduling to provide up to 6x higher throughput and 2x lower latency. Combined with our new usage models, enterprises now have the most flexible, performance-optimized foundation for AI available today.

Ready to evolve your AI infrastructure?

Explore Gcore Everywhere AI or get in touch with our team for a personalized demo of our new integrations and the complete solution.

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