Enterprise AI deployments fail compliance review for one structural reason: the model runs in someone else's infrastructure. On-premise AI solves this by moving the inference layer inside your network perimeter — so prompts, context, and outputs never transit to an external provider. 4MINDS runs on your Kubernetes cluster, uses your GPU nodes, and connects to your identity and secrets management. No external API calls during inference. No shared-responsibility gaps to negotiate.
How 4MINDS addresses the requirements
Zero data egress during inference
Every prompt, context document, and model output stays inside your Kubernetes cluster. No data crosses to an external inference provider at any layer.
GPU-native Kubernetes deployment
Runs on your existing GPU infrastructure — H100, A100, or L40S nodes. No dedicated hardware required from 4MINDS.
Continuous fine-tuning without retraining windows
Ghost Weights trains a shadow copy of your model continuously and swaps it in when it passes automated eval — zero production downtime, full audit trail.
Air-gap capable from day one
Model weights are delivered offline. After initial deployment, zero internet connectivity is required at any layer of the stack.
Your keys, your perimeter
Encryption keys, secrets, and credentials remain under your control in your Vault or KMS. 4MINDS has no access to your deployment.
Regulatory compliance by architecture
On-prem inference satisfies HIPAA, SOX, CUI, and EU AI Act requirements by eliminating data egress — not by negotiating a shared-responsibility clause.
Related resources
See how 4MINDS implements this: Deployment architecture →
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