AI factories need to take in large volumes of raw data and keep it accessible for later processing. This includes everything from images and logs to structured records and telemetry. It’s the first step in turning raw material into a valuable product. 

Most teams handle this using a combination of object storage, file servers, and cold archive. Each has its own protocol and performance tier. That setup works, and in many cases will be the best approach, but it can create friction when data needs to move to the next stage. In some situations, inefficiencies may enter during access control and orchestration, which slows down the whole AI pipeline. 

Platforms like VAST allow you to ingest once and keep data accessible across protocols (NFS, S3, SMB) without creating separate silos. That means less rework, lower overhead, and fewer decisions to revisit later.

Once data is ingested, it needs organising. AI factories prepare data for training by tagging, filtering, batching, and often aligning it with business context.  

Traditionally, this has meant pushing data into a separate warehouse or lake. You extract and transform datasets to get them AI-ready, but this increases latency. It also makes audit trails harder to maintain, which is especially relevant under frameworks like the EU AI Act, which makes data provenance and versioning essential for compliance. 

With VAST, the same system can handle raw ingest, enrichment, and structured access, offering Apache Arrow, Trino, and Apache Spark support. That keeps data close to compute while simplifying schema evolution, catalogue integration, and pipeline rebuilds. Its acceleration of Spark workloads using the VAST DataBase layer pairs particularly well with NVIDIA RAPIDSTM. 

This is the part of the AI pipeline that gets the most attention, since it’s where data is loaded into GPUs for training. But I/O speed is only one part of the picture. AI factories need to feed data to GPUs efficiently, but in most real-world clusters, only a small fraction of GPUs interact with external storage at once.  

Traditional architectures overbuild for peak throughput, layering fast local NVMe with shared flash or parallel file systems. These setups look good on a spec sheet, but much of the data movement during training happens east-west across GPUs, rather than being written back to storage. 

GPUDirect Storage allows for direct-to-GPU reads when it matters, without needing a separate scratch tier. That reduces complexity and avoids redundant data movement. A unified platform also supports multipath access and container-native orchestration, so you can align I/O design with how training jobs actually run. 

Once a model is trained, it starts generating value. Inference workloads tend to be more distributed, with many jobs running in parallel across GPUs or shared infrastructure. These pipelines often operate continuously, consuming fresh inputs and returning outputs with minimal delay. 

In NVIDIA-based environments, especially clusters running NVIDIA Base Command or NVIDIA AI Enterprise, inference runs as a service. That means storage needs to handle high concurrency and predictable latency, not just headline throughput. Using the same storage backend for every workload runs the risk of creating bottlenecks here. 

When unifying inference with the other stages of your data lifecycle, the goal is to maintain consistent performance across workloads – supporting direct access to home directories and model outputs. This is why some platforms offer orchestration-level integration, pairing well with software like NVIDIA Run:ai, especially in multi-tenant environments where GPU scheduling and I/O planning need to align. 

Most AI pipelines don’t end at inference. You’ll want to refine, retrain, or reuse your outputs, especially when the workflow involves feedback loops. That creates a continuous process where storage has to retain relevance beyond the training window. 

In a typical setup, this step is spread across disparate systems: vector DBs live in one place, model checkpoints in another, with logs and lineage data somewhere else entirely. This breaks visibility and slows down compliance teams who need full visibility over data movement. 

VAST allows you to keep this stage inside the same namespace used by training and inference. That simplifies tracking and supports live augmentation pipelines running on NVIDIA infrastructure, whether on a single system or across a full BasePOD. It also ensures that vector outputs and intermediate datasets are audit-ready and available for the next training cycle.