On-Premise LLM Deployment Spec: Infrastructure, Security & Compliance Checklist
Hardware sizing, model selection (Ollama/local deployment), HIPAA/GDPR/SOC 2 checklist, cost modeling vs cloud API.
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Table of Contents & Section Overview
16 Pages TotalHardware & VRAM Sizing Matrix
GPU selection for 8B, 70B, and 120B parameter models.
Inference Engine Server Stack
Configuring vLLM, Ollama, and TensorRT-LLM for throughput.
VPC & Air-Gapped Network Topology
Isolating model servers from outbound internet calls.
HIPAA / SOC 2 Compliance Verification
Audit logging, encryption at rest, and memory zeroing.
Appendix: Kubernetes Deployment Manifests
Helm values and k8s YAML templates.
Problem Statement
"Deploying open-weights LLMs in zero-egress VPCs requires optimizing inference batching while ensuring zero temporary data persistence on GPU VRAM across tenant requests."
Section 3 Excerpt: Air-Gapped Network Topology & Ingress Isolation
Inference server pods must be provisioned in private Kubernetes subnets with no outbound NAT gateway routes. All model weight downloads are pre-staged into internal S3/MinIO storage buckets during deployment build phases.
API access to the model server is exposed exclusively through an internal ingress gateway enforcing TLS 1.3 mutual authentication (mTLS) and OAuth2 service tokens.
Hardware Sizing Guidelines
| Model Parameters | Quantization | Required VRAM | Recommended GPU |
|---|---|---|---|
| 8B Parameters | FP16 / Int8 | 16-24 GB | 1x NVIDIA L4 / A10G |
| 70B Parameters | AWQ 4-bit | 40-48 GB | 2x NVIDIA A10G / 1x H100 |
| 70B Parameters | FP16 (Uncompressed) | 140+ GB | 8x NVIDIA A100 (80GB) |
Related Engineering Resources & Articles
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