New cloud stack cuts AI inference cost, scales enterprise workloads
A new enterprise AI inference stack built on NVIDIA’s Rubin platform is being rolled out by Vultr, aiming to reduce inference costs while improving performance and scalability for enterprise deployments. The solution is available immediately with full-stack AI inference support and is designed to handle the growing demands of agentic AI workloads.
At the core of the deployment is an optimized integration of NVIDIA’s inference ecosystem, including the Dynamo framework and the Nemotron model family. These components are engineered to increase throughput and enable efficient scaling of inference tasks across distributed environments. By combining these with high-performance cloud infrastructure, the stack targets one of the biggest bottlenecks in enterprise AI—cost-efficient inference at scale.
The platform is designed to be flexible, supporting deployment across public, private, and sovereign cloud environments. This makes it suitable for industries handling sensitive or regulated data, where control over infrastructure and data residency is critical. Enterprises can build AI models once and deploy them globally, reducing time-to-market and operational complexity.
The collaboration also extends to data infrastructure through integration with NetApp. Its disaggregated data management platform and AI data engine provide the backend needed to handle large-scale AI workloads. The setup enables in-place data processing with built-in security while maintaining high throughput for GPU-driven inference tasks.
Another component under development is NVIDIA’s open-source NemoClaw stack, aimed at simplifying deployment of always-on AI assistants. It integrates runtime environments and open models to support autonomous agent execution in a secure setup, further pushing enterprise adoption of agentic AI systems.
Support for next-generation NVIDIA Vera Rubin systems is planned for late 2026, which is expected to further enhance performance and efficiency. With a global footprint spanning multiple cloud regions, the infrastructure is positioned to support large-scale AI deployments across geographies.
The move reflects a broader industry shift toward optimized inference stacks, where hardware-software co-design is becoming essential to unlock performance gains and improve AI economics.
