labaisovereignty

Proof-of-Concept: Hosting a Small LLM in an EU Sovereign Cloud (Step-by-Step)

UUnknown

2026-02-22

11 min read

Hands-on PoC lab: provision GPU, encrypted storage, network controls and run an open-source LLM inside an EU sovereign cloud—2026 best practices.

Hook: Why this lab matters now

Short version: You can run an open-source LLM inference inside a European sovereign cloud with commodity steps—if you plan compute, storage, networking, and encryption deliberately. This concise PoC lab shows how to provision a single GPU instance, attach encrypted storage, lock down networking, and run a containerized inference server in an EU sovereign region (2026-era best practices included).

Executive summary (inverted pyramid)

Cloud providers launched sovereign-region options in late 2025/early 2026 (for example, AWS European Sovereign Cloud in Jan 2026). Organizations now need simple, reproducible ways to deploy small LLM inference stacks inside those zones while preserving data residency and cryptographic controls. This lab walks you through a pragmatic PoC: pick a small open-source model, provision a GPU VM in a sovereign region, attach and encrypt block storage, restrict network egress, run a containerized inference server (text-generation-inference or lightweight ggml server), and test with a curl-based client.

What you'll build and why

Goal: A reproducible PoC that runs an open-source LLM inference inside a compliant EU sovereign cloud region.
Scope: Single GPU VM (NVIDIA), encrypted model storage, private networking, basic monitoring, and an inference test call.
Assumptions: You need EU data residency, customer-controlled keys (KMS/HSM preferred), and minimal external egress.

2026 context — why the approach matters

Recent moves in late 2025 and early 2026 accelerated demand for sovereign-region deployments. Cloud vendors added physically and logically isolated regions with enhanced legal guarantees. At the same time, inference optimization trends—widespread 4-bit/8-bit quantization, faster runtimes (Triton, text-generation-inference), and better GPU utilization—mean that a compact PoC can be representative of production concerns.

In short: regulators demand locality and you can now run useful LLMs inside those boundaries without complex re-architecture.

Prerequisites

Account access to a sovereign-region-enabled cloud (example: AWS European Sovereign Cloud, or the provider's EU-sovereign offering).
CLI tools (cloud provider CLI, Terraform optional), SSH key, and basic Linux skills.
Budget: a single GPU VM (e.g., A10G/A100 style) — estimate for a PoC: hours to low-days cost. Use spot/preemptible instances if acceptable.
Model choice: small-to-medium open-source model (7B–13B) for GPU inference or a quantized 4-bit model for reduced memory. Examples: a Llama-family or Mistral-style model—check license and EU-hosting restrictions.

Design decisions (quick checklist)

Instance class: GPU with enough VRAM for your model (7B often fits on 16–24GB with 4-bit quantization; bigger models need 40GB+).
Storage: Encrypted block storage for model files; cold backup in object storage inside the region.
Networking: Private subnet, strict inbound rules, and restricted outbound to approved endpoints (e.g., in-region object storage and container registry endpoints).
Key management: Use customer-managed keys (CMK) in-region or a local HSM to meet sovereignty rules; add LUKS on the VM for double encryption if needed.
Runtime: Containerized TGI (text-generation-inference) or Triton; for ultra-lightweight CC deployments use ggml/llama.cpp on CPU.

Step 1 — Provision infrastructure (Terraform + CLI pattern)

Use Infrastructure as Code so the PoC is repeatable. Below is an abstract Terraform snippet — replace the provider endpoint with your sovereign-region provider configuration and the instance type with the GPU SKU available in that region.

# terraform snippet (abstract)
provider "cloud" {
  region = "eu-sovereign-1"
  # endpoint, credentials, and any sovereign-specific flags here
}

resource "cloud_vpc" "main" {
  cidr_block = "10.10.0.0/16"
}

resource "cloud_subnet" "private" {
  vpc_id = cloud_vpc.main.id
  cidr_block = "10.10.1.0/24"
  availability_zone = "eu-sovereign-1a"
}

resource "cloud_instance" "gpu" {
  ami = "ubuntu-22-04-gpu" # pick provider's GPU image
  instance_type = "gpu.xlarge" # replace with available GPU SKU
  subnet_id = cloud_subnet.private.id
  key_name = var.ssh_key
  tags = { Name = "llm-poc-gpu" }
}

resource "cloud_volume" "model_disk" {
  size_gb = 200
  encrypted = true
  kms_key_id = cloud_kms_key.model_key.id
}

resource "cloud_kms_key" "model_key" {
  description = "CMK for LLM model storage (EU sovereign)"
}

Notes:

Ensure the AMI/image you pick has or supports NVIDIA drivers.
Set encrypted = true and point to a CMK kept in the sovereign region.
Make container registry access private — use in-region container registry (ECR/GCR equivalent) or a restricted registry behind a VPC endpoint.

Step 2 — KMS and encryption at rest

For sovereignty, you must demonstrate that cryptographic keys are controlled within the EU boundary. Two practical approaches:

Cloud CMK: Create a customer-managed key (CMK) in the sovereign region and restrict administrative access to your security team. Configure block volumes and object storage buckets to use that CMK for server-side encryption.
VM-level LUKS: For defense-in-depth, use LUKS to encrypt the model disk on the VM. This ensures keys never leave the VM unless you export them. Good for stricter compliance scenarios.

Example: attaching and LUKS-encrypting the model disk (Ubuntu)

# On the VM after attaching the encrypted volume to /dev/nvme1n1
sudo apt update && sudo apt install -y cryptsetup
sudo cryptsetup luksFormat /dev/nvme1n1
sudo cryptsetup open /dev/nvme1n1 modelcrypt
sudo mkfs.ext4 /dev/mapper/modelcrypt
sudo mkdir -p /mnt/models
sudo mount /dev/mapper/modelcrypt /mnt/models
sudo chown ubuntu:ubuntu /mnt/models

Store LUKS keyfiles in a secure key escrow (HSM or in-region KMS) according to your policy. LUKS adds protection even if the cloud-managed disk encryption was misconfigured.

Step 3 — Networking and least-privilege access

Model downloads, container registry access, and telemetry must be constrained to in-region endpoints. Key points:

Create a private subnet and avoid public IPs on the GPU VM.
Use a bastion host in a separate hardened subnet for SSH access, or better: use session manager or cloud provider's secure remote access service to avoid opening SSH.
Restrict egress with an egress-only firewall/NAT that allows only required endpoints (in-region object storage, container registry, and your management IPs).
Use VPC endpoints (S3/GCS-equivalents) to access object storage privately without leaving the provider network.

Security group example (conceptual)

# Allow only bastion -> GPU SSH and app port 8080 from internal subnet
security_group:
  inbound:
    - { from: bastion-subnet, ports: [22] }
    - { from: private-app-subnet, ports: [8080] }
  outbound:
    - { to: object-storage-endpoint, ports: [443] }
    - { to: container-registry-endpoint, ports: [443] }

Step 4 — Install drivers, Docker and runtime

The inference stack runs best in a container that can access the GPU. Use the vendor-supplied NVIDIA drivers and the nvidia-container-toolkit.

# Ubuntu example
# 1. Update
sudo apt update && sudo apt upgrade -y

# 2. Install NVIDIA driver (use the version recommended for your GPU)
# Example: using the distribution packages or run the NVIDIA installer
sudo apt install -y nvidia-driver-535

# 3. Install Docker
sudo apt install -y docker.io
sudo systemctl enable --now docker

# 4. Install nvidia-container-toolkit
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
# follow repo instructions for your distro
sudo apt update
sudo apt install -y nvidia-container-toolkit
sudo systemctl restart docker

Step 5 — Pull model into encrypted storage

Prefer downloading model artifacts from an in-region object store using signed, short-lived credentials. If your model is large, use parallel download tools and checksum verification.

# Example: using aws s3 (or equivalent) with a pre-signed URL
mkdir -p /mnt/models/my-model
export MODEL_URL="https://inregion-object.example/signed-url"
curl -o /mnt/models/my-model/model.tar.gz "$MODEL_URL"
sync && tar -xzf /mnt/models/my-model/model.tar.gz -C /mnt/models/my-model
ls -lah /mnt/models/my-model

Step 6 — Run an inference server container

For many open-source models, Hugging Face's text-generation-inference (TGI) or NVIDIA Triton is a practical runtime. TGI supports many model formats and is straightforward to run in a container.

# Example: run TGI container (conceptual)
docker run --gpus all --rm -p 8080:8080 \
  -v /mnt/models/my-model:/models/my-model \
  -e MODEL_ID=/models/my-model \
  ghcr.io/huggingface/text-generation-inference:latest

# or run a lightweight ggml server for CPU-only PoC
docker run --rm -p 8080:8080 -v /mnt/models/my-small-ggml:/models my/ggml-server:latest

Note: Provide the runtime with limited cloud credentials if it needs to refresh models from storage. Use instance roles with least privilege.

Step 7 — Test inference

Use a simple curl request against the server's HTTP endpoint to validate the flow. This confirms networking, container, and storage all work and stay within the sovereign boundary.

# Example curl for TGI-style HTTP API
curl -s -X POST "http://127.0.0.1:8080/v1/models/my-model:predict" \
  -H "Content-Type: application/json" \
  -d '{"inputs":"Translate to German: The cloud PoC is operational.", "parameters": {"max_new_tokens":40}}'

Step 8 — Observability, audit, and compliance

To prove compliance you need logs and auditable controls:

Enable in-region audit logs (API activity, KMS access logs, and object-store access logs).
Use host-level monitoring: collect GPU metrics (nvidia-smi exporter), container logs, and process-level telemetry. Forward metrics to an in-region monitoring destination.
Retain logs according to your retention policy and store them in encrypted, immutable storage if required.

Step 9 — Optimizations for cost & performance

Make your PoC realistic for production by applying common 2025–2026 optimizations:

Quantize models to 4-bit or 8-bit to dramatically reduce VRAM needs and increase throughput. Libraries like GPTQ, AWQ and bitsandbytes are now standard.
Batching and concurrency: Tune your server batching parameters to maximize GPU utilization without increasing latency beyond SLAs.
Spot/preemptible GPUs: Use spot instances for non-critical workloads and add an autoscaler that can replace interrupted nodes.
Use sharding or tensor-slicing only when you need larger models; prefer single-node quantized models for simplicity in sovereign PoCs.

Step 10 — Scale and productionize (next steps)

If the PoC succeeds, consider these production patterns:

Kubernetes with GPU nodes: Use K8s and a GPU device plugin for scale; deploy the inference server as a StatefulSet or Deployment with node selectors.
Autoscaling & orchestration: Use cluster autoscaler/Karpenter and spot capacity pools; keep model artifacts in a versioned in-region object store.
Key lifecycle: Set up key rotation and HSM-backed keys if compliance requires it.
Drift detection & IaC policy: Enforce policies with OPA/Gatekeeper to ensure workloads remain inside sovereign boundaries.

Practical security checklist (copy into your PoC repo)

CMK exists and is region-locked; disk and object storage encryption use the CMK.
No public IPs on inference VMs; bastion or session manager is enforced.
Egress is restricted to in-region endpoints via firewall rules and VPC endpoints.
Access keys rotated and ephemeral; no long-lived secrets in containers.
Audit logs capture KMS use, instance creation, and object fetches.
Model licensing and data residency checks were completed before model download.

Real-world considerations & pitfalls

Provider coverage: not all sovereign regions offer the same GPU SKUs or managed services (e.g., GPUs might lag mainstream regions). Validate available SKUs early.
Latency: test latency from your application's network footprint to the sovereign region—if clients are global, you may need a hybrid strategy.
Model updates: ensure model pull workflows and possibility of in-region caching for large artifacts.
Legal & procurement: confirm the provider's contractual assurances meet your compliance requirements—technical controls are only part of the story.

Example cost & size guidance (2026)

Small PoC (7B quantized) — 16–24GB GPU, single instance: low hourly cost; fits in 100–200GB model disk.
Medium PoC (13B) — 40GB+ GPU or quantized 8-bit on 24GB GPU with offloading: moderate hourly cost.
Large models (70B+) — multi-GPU or production-managed inference; significant cost and orchestration complexity.

Actionable takeaways

Start with a 7B quantized model to validate the end-to-end flow inside the sovereign region before scaling.
Use both cloud CMK and VM-level LUKS for layered protection—this speeds audits and raises assurance levels.
Lock egress and use VPC endpoints so model download and container pulls never leave the sovereign network path.
Automate the PoC with Terraform and shell scripts so you can reproduce the demonstration for auditors or stakeholders.

Final checklist before stakeholder demo

Proof: run a curl request from a client inside the same region and show inference logs.
Security: demonstrate CMK usage and show an audit log line proving a model file access inside region.
Cost: present an estimated monthly burn for a production variant using spot + autoscaling setups.
Compliance: show a table mapping controls to legal requirements (data residency, key locality, access auditing).

Closing thoughts — future-proof your sovereign LLM PoC

By 2026 the ecosystem lets you balance sovereignty and agility: quantized models reduce hardware needs, and containerized runtimes let you iterate fast. The real work is governance—making sure keys, logs, and artifacts are provably within the EU boundary. Start small, automate everything, and document your decisions for auditors and engineers who will inherit the environment.

Call to action

Ready to run this PoC? Clone a starter repo that includes Terraform templates, encrypt-and-mount scripts, and an example container manifest to run TGI. If you want, I’ll generate the repo customized for your sovereign provider and GPU SKU—tell me your provider and the minimum GPU VRAM you have available and I’ll output a ready-to-run folder with docs and scripts.

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