Teams can access the same dataset simultaneously — whether writing from sensors, loading training data into GPUs, or serving models into production — via file or object protocols. No ETL, duplication or added complexity.
Overview
VAST Data Platform for AI Training
The Proven Platform for Training Advanced AI Models
Training AI models at scale requires a new foundation: The VAST AI Operating System. Designed for the demands of exabyte-scale data and massive GPU clusters, VAST unifies storage, database, and compute into a single, simplified layer. Powered by our DASE architecture, VAST eliminates complexity and provides the infrastructure intelligence needed for continuous AI development and deployment.
Trusted by the world’s leading artificial intelligence organizations
View More Customers
Servicing over 1 Million GPUs, VAST Data is the standard for the world's most demanding AI Cloud Service Providers
Overview
Why Architecture Matters for AI Training
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.
How VAST Eliminates AI Training Delays
VAST AI Operating System is built upon the revolutionary Disaggregated Shared-Everything (DASE) architecture, purpose-built to solve these problems.
DASE decouples compute from capacity. As GPU clusters grow, you can scale storage performance on demand without over-provisioning capacity, rebalancing data, or interrupting live training. This lets model builders keep GPU utilization high throughout the training cycle, regardless of GPU cluster size, while also adding capacity independently as source, prep, and training data volumes grow.
What makes the VAST AI OS perfect for AI training is its ability to keep up: not just with data scale, but also with the pace of iteration. When infrastructure doesn’t get in the way you reach the next breakthrough faster.
Our Growth Story
Unify and Simplify Your AI Workflow. Maximize GPU Utilization.
AI training doesn’t begin or end with storage. Before training, you need to ingest, clean, organize, and label. After training, you need to evaluate, analyze, and serve. Every handoff, copy, ETL job, or silo slows progress and reduces effective GPU capacity utilization. Every delay in deploying infrastructure, tuning for performance, or recovery from failure adds complexity.
VAST removes these barriers by delivering a unified AI operating system that keeps your entire AI pipeline moving smoothly from start to finish.
Multi-Protocol Access Without the Middle Steps
Native Streaming Eliminating Message Bus Clusters
Allow streaming data to write directly into the VAST DataBase via a Kafka-compatible API, then query it in place — no external tools required. Embedded services provide instant visibility with full historical context.
Less Infrastructure, Fewer Delays
Reduce operational overhead by consolidating core services into a single platform. Fewer systems to manage means faster model delivery, lower costs, and simpler scale-out.
Deploy Once, Scale Without Rebuilding
Eliminate downtime spent tuning, scaling, and rebalancing. Our architecture lets infrastructure teams go from hardware delivery to active training in a fraction of the time.
Built for Service Providers and Model Builders
Service providers can deliver multi-tenant GPU-as-a-service at scale, with over 99.999% availability and QoS to meet SLAs. Model builders benefit from built-in reliability, automation, and observability.
Multi-Tenancy, QoS, and Observability Engineered
Built for AI service delivery. Offers secure multi-tenancy, customizable QoS for SLAs, and dedicated tenant level observability, ensuring stable, high-performing AI environments for every tenant.
Resources
Innovation begins with understanding
Blog Post
A Checkpoint on Checkpoints in LLMs
Deep learning models are massive, requiring efficient parallelization and recoverability. VAST explores how parallelism impacts checkpoint and restore in complex models.
Jan 10, 2024
Analyst Paper
NAND Research Report: Solving AI Data Pipeline Inefficiencies
NAND Research’s report shows how optimizing data infrastructure can streamline AI workflows, cut costs, and accelerate insights for enterprises and service providers.
15 pages
Analyst Paper
Navigating the Era of AI Infrastructure
Explore how VAST Data's all-flash system and VAST DataBase are revolutionizing enterprise data infrastructure for AI, delivering scalable, high-performance solutions.
10 pages