AI organizations are building massive models on multi-billion-dollar, gigawatt-scale GPU clusters. The challenge is keeping those GPUs productive while managing data at exabyte scale. The VAST AI Operating System is the unified data platform that powers every phase of model development with the performance to keep GPUs fully utilized, the security to protect your IP, and 99.999% uptime that ensures continuous AI development.
Delivering 99.999% uptime across over 2 million GPUs, VAST Data ensures your billion-dollar AI infrastructure never sits idle due to storage failures.
To train competitive AI models, you need to move massive volumes of data to thousands of GPUs continuously, without interruption, at consistently high throughput.
That sounds simple. But at exabyte scale, most infrastructure breaks down. Storage becomes the bottleneck. Traditional checkpoint approaches interrupt progress. Scaling performance means scaling everything, including what you don’t need. Tuning becomes constant. And every delay slows the path to your next model.
VAST AI Operating System eliminates these bottlenecks through purpose-built architecture for AI training at scale.
Our Disaggregated Shared-Everything (DASE) architecture separates compute from capacity, enabling independent scaling without data rebalancing or training interruptions. Asynchronous parallel checkpointing delivers rapid recovery from GPU failures while practically eliminating checkpoint overhead. Your GPUs stay productive through every training phase.
The VAST AI OS supports files, objects, databases, and streaming data—no integration complexity or manual data movement between silos. Built-in event-driven automation accelerates pipeline management. Complete observability pinpoints performance issues. Cryptographic security and full data lineage provide compliance and IP protection. 99.999% uptime ensures storage reliability matches your GPU cluster demands.
The result: faster time to model, maximum GPU utilization, and engineering focus where it belongs: on building better AI.
AI model building spans data ingestion, curation, continuous training, and global deployment. Every handoff between disparate systems, manual data movement, or storage bottleneck wastes expensive GPU cycles. Infrastructure failures and operational complexity divert engineering resources from innovation.
VAST removes these barriers with a unified AI Operating System powering every stage of model building, keeping billion-dollar GPU clusters continuously productive from data preparation through training to deployment.
VAST's disaggregated architecture enables asynchronous checkpoint writes that eliminate GPU idle time during training. By separating compute from storage, VAST ensures GPU clusters remain productive without waiting for storage operations, delivering faster time-to-model and maximum ROI on GPU investments.
VAST integrates files, objects, databases, and streaming data into a single platform, eliminating the complexity of managing multiple storage vendors and manually moving data between systems. This provides consistent performance across every stage of the AI lifecycle.
VAST's DASE architecture delivers over five-nines uptime, ensuring critical AI workloads never stall due to infrastructure failures. This reliability, combined with integrated observability required for running continuous training operations, allows infrastructure architects to confidently maintain production GPU clusters.
VAST's cryptographic multi-tenancy, immutable snapshots, and VAST Catalog provide comprehensive security and governance for protecting proprietary models and meeting compliance requirements. Complete data lineage and audit trails deliver the visibility and control to secure valuable AI IP.
VAST DataEngine uses event-driven triggers and serverless functions to automate data transformation and validation, augmenting tools like Airflow and Prefect to create adaptive, real-time data pipelines.
VAST DataSpace provides a single namespace spanning on-premises and cloud environments, enabling teams to access data and serve models anywhere without replication or migration complexity. Intelligent streaming moves data only when required, maintaining consistent performance across distributed training and inference deployments.
Based on an analysis of 85k real-world checkpoints, the blog shows that large-scale LLM training requires storage bandwidth well below 1 TB/s due to asynchronous checkpointing, allowing system designers to save resources for more GPUs.
NAND Research’s report shows how optimizing data infrastructure can streamline AI workflows, cut costs, and accelerate insights for enterprises and service providers.
Explore why Fluidstack, an AI cloud platform, selected and uses the VAST AI OS to provide the necessary scale, performance, and reliability for its customers' large-scale AI workloads.