Presented by VAST Data
AI workloads compute in ways that look nothing like the systems running enterprise applications over the past few decades. We’re rapidly moving into a world of millions of GPUs — deployed everywhere from cloud AI factories to edge devices — participating in continuous loops of inference, decision-making, and model refinement.
These environments aren’t driven by traditional enterprise software. They require systems capable of ingesting and processing enormous volumes of unstructured, real-world data — imagery, video, telemetry, and text — in real time, at a scale measured in exabytes. And legacy infrastructure, built for transactional and analytical workloads, simply can’t keep up.
That’s why a new kind of operating system is needed — one designed for AI’s unique demands on data, compute, and infrastructure.
What’s holding AI infrastructure back?
The biggest infrastructure challenges facing AI today aren’t hardware limitations. They’re rooted in systems design. Most of today’s infrastructure still follows a “shared-nothing” model, popularized by internet pioneers like Google in the early 2000s. In this approach, data is split into partitions and distributed across servers to enable horizontal scaling.
It was ideal for the problems of that time but doesn’t scale cleanly for AI environments, where millions of processors need concurrent access to shared data. As these traditional clusters grow, so does the coordination overhead, creating performance bottlenecks for real-time, high-concurrency workloads like AI agent inference and continuous feedback loops.
VAST Data saw this challenge early and created an alternative: a disaggregated, global data platform purpose-built for AI.
A shift to disaggregated, parallel architectures
The solution requires a different approach. Rather than partitioning data across servers, what if every processor could access every byte of data in parallel, without the need for east-west traffic between nodes? And what if this were possible using standard networks and commodity hardware?
This concept led to a new architecture known as DASE (Disaggregated and Shared-Everything). VAST Data pioneered this approach, separating compute from storage while making all data globally accessible at high speed. It enables CPUs and GPUs to read and write data directly without coordination delays. No partitions, no dependency chains, and no cascading slowdowns as systems grow to tens of thousands of processors.
It also delivers significant improvements in resilience, cost efficiency, and real-time access. VAST’s disaggregated infrastructure supports advanced data protection schemes and global erasure coding, lowering storage costs while increasing reliability. It can ingest, process, and serve massive data volumes without sacrificing consistency or availability — two essential requirements for modern AI platforms.
From storage platform to AI operating system
Early adopters initially saw VAST as a next-generation, high-performance storage platform. But its creators envisioned something more ambitious: a data platform operating system for AI infrastructure.
Just as operating systems in the past managed CPUs, memory, and storage for conventional applications, the AI OS must orchestrate data, compute, and AI agents across vast, distributed environments while maintaining governance, security, and real-time responsiveness.
This isn’t a theoretical idea. It’s already taking shape in the VAST AI Operating System, where a scalable, data-centric foundation supports not just storage, but compute and AI runtime services. The VAST DataEngine provides a containerized environment for deploying distributed Python functions and microservices at massive scale. The VAST InsightEngine turns unstructured data into AI-ready context by generating vector embeddings in real time. And the new VAST AgentEngine delivers the runtime and tooling to deploy and manage AI agents in enterprise environments.
These aren’t isolated services. They’re integrated components designed to operate on a disaggregated, parallel infrastructure built for AI’s growth. The result is a platform capable of powering real-time decisioning, vector search, AI agent orchestration, and secure, multi-tenant data services — all from a unified foundation built by VAST Data.
The new standard for AI infrastructure
As AI adoption accelerates, enterprises face a choice. They can continue retrofitting legacy systems built for older workloads or move to new infrastructure built specifically for agent-based, real-time AI computing at massive scale.
The AI operating system is no longer a concept waiting to be defined. VAST is delivering it. It’s quickly becoming a requirement for organizations building intelligent, autonomous systems. And for those placing AI at the core of their future, the VAST AI Operating System will serve as the foundational layer for model training, inference, intelligent applications, and autonomous decision-making.
In the same way Linux and Windows once defined the operating systems of their eras, AI needs a platform designed for intelligent, real-time, high-scale operation. VAST Data’s vision for this new era is already here — and it’s setting the new standard for AI infrastructure.
Aaron Chaisson is VP Product and Solutions Marketing at VAST Data.
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