Hardware · Thermal duty
DGX Spark Thermal Management
Prevent thermal degradation on soldered unified memory during 24/7 continuous LLM inference workloads. Active cooling chassis and telemetry scripts.
Repository · Open patterns & guides
Lab Blueprints, FAQs & Hardware Guides
A practical index of hardware thermal management, document RAG patterns, personal legal threat models, and operational tips. Designed to help you run local models on hardware you control without defaulting to public chat products.
Hardware & infrastructure engineering
1 BlueprintDocument & citation first RAG workflows
2 BlueprintsMedical records · Veterans
VA Claims Record Workflow
Organize Service Treatment Records (STRs) and medical files into a citation first local vault before filing claims.
Governance · Board vault
HOA Document Workflow
Organize bylaws, meeting minutes, and financial reserve audits in a board controlled vault with clear security boundaries.
Personal legal & threat modeling
1 BlueprintLegal · Asset security
Divorce Data Security Plan
Threat model personal financial ledgers, legal strategy, and communication records during divorce litigation.
Frequently asked questions & deployment tips
Why run local open weight models instead of commercial cloud APIs?
Commercial cloud APIs require sending your sensitive text over third party networks under evolving terms of service. Local open weight models (such as Llama 3, Mistral, or Qwen) allow full air gapped processing, zero network egress, and total control over data retention.
What hardware is required for high speed local document retrieval?
For desktop setups, workstation nodes with unified memory or dedicated GPUs with 16GB to 128GB VRAM (such as RTX 4090, Mac Studio, or NVIDIA DGX Spark) can run 8B to 70B parameter models at 30 to 80 tokens per second locally.
How do you prevent model hallucinations in critical document reviews?
Enforce retrieval boundaries: convert documents into structured vector chunks, require exact page and passage citations for every model response, and program the API server to output a structured null when the source text does not support the query.
Need a custom on premise AI pipeline?
Discuss your secure LLM deployment requirements with an experienced systems engineer.
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