Sterling Federal SI: Gaudi 3 Reference Architecture
How Sterling deployed Blockify on Intel Gaudi 3 to create a reference architecture for federal AI operations with classification marking preservation.
Federal AI Challenges
Sterling needed to demonstrate enterprise-scale AI processing for classified environments where data governance, classification markings, and air-gapped operation are non-negotiable.
Blockify on Intel Gaudi 3 accelerators transformed millions of pages into AI-optimized knowledge bases with full RBAC and classification marking preservation (SECRET, TS/SCI, NATO).
Implementation Areas
Data Processing
- 500K pages in 8.5 hours
- Classification marking preservation
- RBAC integration
- Multi-format ingestion
Security
- SECRET/TS/SCI support
- NATO-restricted content
- 100% on-premise
- Red Hat OpenShift AI
Gaudi 3 Performance
500,000 pages were processed in 8.5 hours on Gaudi 3 -- a 48x throughput improvement versus Xeon, which would have taken 17+ days for the same workload.
Key Outcomes
- Throughput: 48x throughput improvement vs Xeon
- Scale: 17.8 million pages/month on single Gaudi 3 core
- Classification: Classification markings preserved throughout AI pipeline
- Accuracy: 78x accuracy improvement for intelligence analysis
Download the Full Sterling Federal SI Case Study
Get complete details on Gaudi 3 reference architecture for federal AI.
- 500K pages in 8.5 hours
- 48x faster than Xeon
- Classification marking preservation
- SECRET/TS/SCI/NATO support
- Red Hat OpenShift AI deployment
Get Your Free Copy
Enterprise AI for Federal Operations
See how Blockify on Gaudi 3 can transform your operations.
Request a Demo