Private AI Infrastructure Blueprint
You run models locally. Here is how to run them in production.
If you already spin up local LLMs on a workstation, you know the ceiling: VRAM caps, no data-governance story, and nothing an auditor will sign off on. Petronella Technology Group built this blueprint from the exact hardware and hardening we deploy for regulated teams - so private AI moves from your desk to a system your compliance officer trusts.
- Reference build sheets for GB10 Grace Blackwell cluster nodes and RTX-class inference workstations
- Two-node clustering over QSFP112 400G to pool 128GB unified memory into 256GB for larger models
- Security-hardening checklist for open-weight model hosting: isolation, secrets, logging, egress control
- Data-sovereignty map for CUI, PHI, and PII aligned to CMMC, HIPAA, and DFARS
Get the Blueprint
Free. Delivered instantly to your screen and inbox. Built for engineers and IT leads, not marketers.
A build-and-harden reference, not a sales brochure
Every section maps to hardware Petronella Technology Group actually deploys and to controls a defense or healthcare auditor will ask about. No vendor lock-in pitch - the blueprint works whether you build it yourself or have us deliver it turnkey.
Cluster Node Build Sheets
GB10 Grace Blackwell reference nodes with 128GB unified memory, the QSFP112 400G interconnect for two-node clustering, and where RTX 5090 and RTX 6000 workstations fit for single-box inference.
Model Hosting Stack
Running open-weight models such as Llama, Qwen, Mistral, and DeepSeek on hardware you own, with retrieval over your own documents instead of a third-party API.
Security Hardening Checklist
Network isolation, secrets handling, prompt and access logging, and egress control - the Petronella Private AI hardening pattern applied to a self-hosted stack.
Data Sovereignty and Compliance
Where CUI, PHI, and PII may live, how on-prem hosting maps to CMMC, HIPAA, and DFARS obligations, and the evidence an assessor expects to see.
Eight sections, start to production
- 01Why private AI beats the public API for regulated data
- 02GB10 cluster node reference build sheet
- 03Two-node clustering over QSFP112 400G
- 04Inference workstation sizing (RTX 5090 / RTX 6000 / H200)
- 05Open-weight model selection and serving
- 06Security-hardening checklist for self-hosted AI
- 07Data-sovereignty map for CUI, PHI, and PII
- 08From prototype to audited production
The hardware and services behind the blueprint
The blueprint references real systems. Explore the build pages and the Petronella Private AI offering:
Questions engineers actually ask
Is this a real technical document or a lead-gen teaser?
It is a working reference. The build sheets, interconnect specs, and hardening checklist are the same ones Petronella Technology Group uses on client deployments. You can build from it yourself.
Do I have to buy hardware from Petronella?
No. The blueprint is vendor-neutral on procurement. We are happy to deliver the whole stack turnkey, but nothing in the document requires it.
Will my data ever be used to train someone else's model?
No. The entire premise is open-weight models you own and operate. Your prompts, documents, and conversations stay inside your environment.
Can this satisfy CMMC or HIPAA requirements?
On-prem private AI is a strong foundation for handling CUI and PHI, and the blueprint maps the relevant controls. Formal readiness still needs a scoped assessment - the guide shows where that line sits.