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Guides to local AI deployment.
Plain explanations of what it takes to run language models on hardware you own: air gaps, hardware shapes, compliance, cost, and how to evaluate without a demo.
- Guide / Deployment
Air-gapped LLM deployment: what it means and what to verify
What an air-gapped LLM deployment actually requires, how a local inference system works with the network disconnected, and what a buyer should verify before trusting the label.
Updated November 5, 2026 · 3 min read - Guide / Evaluation
How to evaluate a local AI system with one workflow
A practical method for evaluating a local inference system: pick one real workflow, define the evidence you need, run it on hardware like yours, and decide on measured results rather than demos.
Updated November 5, 2026 · 3 min read - Guide / Compliance
Local AI for regulated industries: HIPAA, GDPR, ITAR, CMMC, GLBA, NIS2
How a local inference system supports the data-handling requirements behind HIPAA, SOC 2, GDPR, ITAR, CMMC, GLBA, and NIS2, and what a security review should ask for.
Updated November 5, 2026 · 3 min read - Guide / Decision
Local inference vs hosted AI APIs: how regulated teams decide
A plain comparison of running language models on hardware you own versus calling a hosted AI API, covering data boundaries, cost structure, control, and what each path is good at.
Updated November 5, 2026 · 3 min read - Guide / Hardware
Sizing hardware for local LLM inference: laptop, shared node, or private cluster
How to choose between a laptop, a shared node, and a private cluster for local language-model inference, and why the workflow, not the model, should drive the sizing.
Updated November 5, 2026 · 3 min read