How Everpure plans to stop AI from starving without data
Article excerpt
SPONSORED SPONSORED FEATURE: The vendor's AI solutions are dedicated to increasing GPU utilization and avoiding costly GPUs doing nothing while waiting for data Imagine you are an AI Agent. You execute inside a $20-40 million-plus Nvidia SuperPOD system’s accelerator hardware, have a skill set and can call up other agents to do your bidding. An insurance company customer, using a PC browser or smart phone, asks a question: “Am I covered for weather-related damage under my current policy?” and it is handed off to an AI agent. What happens next? The agent itself runs on clusters of CPU + GPU/accelerator servers that host the large language model, any Retrieval-Augmented Generation (RAG) components, orchestration logic, and tools that look up the actual policy data. Those same servers (or tightly coupled backend systems) access the policy database/storage to answer the weather insurance coverage question, then return the response to the user’s device. Everything the agent does depends upon data, and it and all the other agents operating at the same time in the SuperPOD system, need to get that data off a storage system. A national or large regional-level insurance company will have petabytes, even exabytes, of data it stores so it can manage its insurance business. There needs to be a central index of this data’s structure, state, location, field names, types and semantics so...
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Everpure Key Value Accelerator (KVA) offloads the cached token states directly to shared flash over NVIDIA GPUDirect Storage (GDS) via RDMA.
