Randy Hayes, vice president of public sector at VAST Data Federal, said federal agencies must modernize their underlying data architecture to fully realize the promise of artificial intelligence.
In an article published on Carahsoft.com, Hayes wrote that many agencies still rely on legacy storage systems built around fragmented tiers of hard drives, flash and tape, which spread critical information across disconnected silos and contribute to “sampling bias.”
“When information is trapped in silos, agencies are often forced to train AI models on only a slice of their total data corpus,” Hayes wrote, adding that fragmented architectures also create complex integrations and fragile data pipelines that can delay mission-critical decisions.
How Does VAST Data Federal’s Single Data Fabric Work?
Hayes said VAST Data Federal addresses these challenges by replacing fragmented infrastructure with a unified data fabric designed for AI workloads.
The platform consolidates data services into a single high-performance architecture that eliminates traditional tradeoffs between accessibility, scalability and speed. By creating a unified data layer, agencies can manage and access information across edge locations, core data centers and cloud environments.
What Are the Key Components of VAST’s AI-Optimized Data Platform?
Hayes outlined several capabilities of its AI operating system designed to support AI deployments across government environments. One component is an exascale vector database that turns complex data into vector representations that AI models can process more efficiently.
The platform also includes DataSpace, which creates a unified namespace spanning the edge, cloud and core data center; DataEngine, which supports automated workflows; and AgentEngine, which enables agencies to deploy and manage autonomous AI agents as federal employees begin working alongside AI systems.
How Does VAST Address the KV Cache Challenge?
Hayes said agencies deploying large AI models must also address a growing infrastructure challenge related to inference performance. While graphics processing unit, or GPU, compute power remains critical, he noted that system performance increasingly depends on how efficiently infrastructure can move and manage key-value, or KV, cache data generated during inference workloads.
AI models produce large volumes of output and repeated calculations when processing tokens. Although KV caching accelerates processing by storing previous computation results, Hayes said the process often leads to redundant data accumulating in expensive GPU memory. VAST addresses this issue by enabling the KV cache to persist and scale across GPUs and compute nodes rather than remaining isolated in local GPU memory.
Hayes said this approach reduces redundant computations and allows agencies to reuse previously processed tokens in retrieval-augmented generation applications. He added that the platform integrates with agencies’ existing infrastructure and supports legacy storage systems, databases and servers without requiring a full technology replacement.
