VAST Data has introduced a new inference architecture designed specifically for Agentic AI the next generation of artificial intelligence that requires long-term reasoning and persistence. This architecture powers the new NVIDIA Inference Context Memory Storage Platform, establishing a high-performance standard for AI-native storage.
Breaking the Inference Bottleneck
As AI transitions from simple chat responses to complex, multi-agent workflows, traditional storage can’t keep up. Performance now depends on the KV cache (inference history). If this context is stuck in a single GPU’s local memory, the system slows down.
Built on NVIDIA BlueField-4 DPUs and Spectrum-X Ethernet, the VAST architecture allows for “gigascale” inference.
Key benefits include:
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Reduced Time-to-First-Token (TTFT): By embedding the VAST AI OS directly into the DPU, data moves faster between the GPU and storage.
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Global Context Sharing: Using Disaggregated Shared-Everything (DASE) architecture, every host in a network can access a shared memory pool without the usual coordination delays.
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Power Efficiency: Optimized data paths reduce the energy overhead typical of massive AI clusters.
Also Read: Lexar Introduces Industry’s First “AI Storage Core” for Next-Generation Edge AI Devices
Why “Context” is the New Performance Frontier
“Inference is becoming a memory system, not a compute job,” says John Mao, VP of Global Technology Alliances at VAST Data. In this new landscape, the most successful AI deployments won’t just be the ones with the most GPUs—they will be the ones that can move and govern context at line rate.
Kevin Deierling, Senior VP of Networking at NVIDIA, likens this to human memory: “Just like humans write things down to remember them, AI agents need to save their work. Multi-turn inferencing transforms how we manage memory at scale.”
Enterprise-Grade Governance
Beyond speed, this architecture provides the “boring but essential” tools for enterprise adoption:
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Policy Controls: Manage who can access specific AI context.
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Isolation & Security: Ensure data privacy in multi-tenant environments.
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Auditability: Track the lifecycle of data used in automated decision-making.
