Enterprise AI is moving rapidly from experimentation to real-world deployment. As organizations operationalize models, the focus is shifting toward inference performance, scalability, and infrastructure efficiency. While training often gets the attention, inference is where AI produces continuous value and where infrastructure decisions ultimately impact outcomes.

As part of this transition, Vultr is expanding its collaboration with NVIDIA while welcoming VAST Data into the Vultr Cloud Alliance. By combining NVIDIA’s Dynamo inference framework and Nemotron model family with the VAST AI Operating System and Vultr’s globally available cloud compute and cloud GPU infrastructure, the partnership aims to support large-scale, data-intensive AI workloads with greater efficiency.

Together, the companies are aligning high-performance GPU infrastructure with a unified data platform designed to keep pace with modern AI systems.

Data-intensive AI workloads require a new infrastructure model

As AI models grow larger and inference pipelines become more complex, infrastructure challenges often shift away from compute and toward data movement and orchestration. Large datasets, distributed teams, and growing performance expectations can introduce bottlenecks that slow development and increase operational complexity.

VAST focuses on removing the operational friction between data, memory, and compute so that intelligent systems can run continuously and reliably. Vultr focuses on making high-performance cloud infrastructure simple, accessible, and globally available for organizations building the next generation of applications and AI platforms.

By pairing the VAST AI Operating System with Vultr’s globally distributed cloud compute and GPU infrastructure, customers gain an environment designed to support AI workloads that depend on sustained data throughput and consistent performance.

Improving inference efficiency with NVIDIA Dynamo and Nemotron

Efficiently scaling inference is becoming a central concern for enterprises deploying generative AI and large language models. Even when compute capacity is available, many organizations encounter limitations around token throughput, latency, and GPU utilization.

NVIDIA’s Dynamo inference framework helps address these challenges by improving how inference workloads are scheduled and executed across GPU infrastructure. The Nemotron model family complements this by providing open, enterprise-ready models optimized for domain-specific use cases.

When deployed on Vultr Cloud GPUs and supported by the VAST AI Operating System, these technologies create a tightly integrated environment where compute, models, and data pipelines operate together rather than as isolated systems.

This alignment helps organizations deliver faster inference performance while improving the overall efficiency of GPU resources.

Simplifying the foundation for AI pipelines

Many enterprises still rely on fragmented infrastructure stacks that separate storage, compute, orchestration, and data processing across multiple systems. This fragmentation often introduces operational overhead and limits the efficient scaling of AI workflows.

The combined Vultr and VAST environment provides a simpler foundation for AI pipelines by allowing organizations to run preparation, training, inference, and emerging agent-based workflows within a unified architecture.

Customers benefit from:

• High-performance AI compute and data operating as one system: Vultr Cloud GPUs and Bare Metal provide accelerated compute for training and inference, while VAST ensures data and context remain continuously accessible.
• A unified environment for AI workflows: Data preparation, training pipelines, inference services, and agent-driven applications can run within a single integrated infrastructure layer.
• Infrastructure designed for production AI: The platform supports growing model sizes, expanding datasets, and more demanding workloads without requiring constant re-architecture.

This approach reduces the complexity of managing AI infrastructure while allowing organizations to scale workloads as their models and data environments evolve.

Built for continuous, data-driven AI systems

Modern AI systems are increasingly interactive and continuous. Instead of responding to isolated prompts, many applications now involve persistent reasoning, multi-step workflows, and dynamic interactions with large data sources.

These patterns place new demands on infrastructure, including:

• Continuous access to large datasets
• High-throughput inference pipelines
• Reliable data availability for context-aware AI
• Efficient GPU utilization across distributed systems

VAST addresses these needs through its AI Operating System, which unifies storage, data services, and execution environments in a platform optimized for data-intensive AI workloads. When paired with Vultr’s globally available GPU infrastructure, the result is an environment designed to keep compute resources productive while minimizing data bottlenecks.

Supporting AI across industries and global environments

The collaboration between Vultr and VAST is designed to support organizations building and scaling AI platforms that rely on constant interaction between models, data, and compute.

This includes environments focused on:

• Large-scale model training
• Real-time inference services
• Data-intensive AI pipelines
• Emerging agent-based AI systems

These workloads are increasingly common across industries such as healthcare, financial services, media, research, and AI-native startups.

With Vultr’s global infrastructure footprint, organizations can deploy AI services close to users and datasets while maintaining consistent performance across regions.

Enabling long-term AI infrastructure

As AI systems evolve, infrastructure must support not only current workloads but also the rapid growth of data, model complexity, and compute demand.

By aligning cloud-scale GPU infrastructure with an AI-optimized data platform, the partnership between Vultr and VAST provides a foundation designed for long-term AI development.

Combined with NVIDIA’s inference optimization technologies, the stack enables enterprises to build AI environments that prioritize performance, efficiency, and scalability while supporting the continuous interaction between models, data, and compute that modern AI systems require.