Your NVIDIA AI Factory Has a 40-70% Throughput Leak
The inference inflection has arrived. Tokens are the new output of the AI factory. But redundant preprocessing is stalling your production line, wasting Blackwell and Rubin GPU cycles on work that was already done.
The Problem: Inference Amnesia
The problem is not the model - it is the supply chain.
In 2026, AI is agentic. A single task can trigger hundreds of inference calls across RAG agents, compliance agents, analytics agents, and search agents. Each one re-derives the same context from scratch.
This inference amnesia wastes 40-70% of your Blackwell and Rubin GPU cycles on redundant work before any reasoning begins.
Infrastructure Amnesia in the Inference Era
Why MetadataHub
Process Once, Maximize Tokens per Watt
Cut 40-70% of redundant preprocessing so your GPUs focus on reasoning and generation.
One Intelligence Layer for All Agents
Every NIM microservice and agent queries the same authoritative source instead of re-deriving context.
Activate Dark Data at Scale
Turn petabyte archives into instantly usable context without migration.
Runs in Your Environment
On-prem or in your cloud, alongside your existing GPU infrastructure. Your data never leaves your control.
Calculate Your Inference Waste
See how much GPU capacity you are losing to redundant preprocessing.
Estimated Annual Savings
$1,320
Adjust values in Framer property controls to update the calculation.
Measured Impact from Production Deployments
Reduction in redundant processing
Fewer archive recalls
Typical payback period
Trusted by Leading Research Institutions
Managing 200 petabytes meant our scientists repeatedly reprocessed the same files. MetadataHub changed that - we can now access information instantly without touching the underlying storage.
- Carsten Schaeuble, Head of Group, IT and Data Services, Zuse Institute Berlin
In seconds I can find 80,000 images with exactly 300 nm resolution - something I could never do before MetadataHub.
- Dr. Yannic Kerkoff, Researcher, Zuse Institute Berlin