Cloud Server for AI Inference in Europe: GPU & CPU Guide

Cloud Server for AI Inference in Europe: GPU & CPU Guide

AI inference - running a trained model to generate predictions or completions - is one of the fastest-growing server workloads in 2026. For businesses operating in Europe, the infrastructure choice involves more than hardware specs: GDPR requires that inference requests containing personal data be processed on infrastructure under EU jurisdiction.

This guide covers how to choose between GPU and CPU inference, what hardware each approach needs, and how to serve AI models in production on a GDPR-compliant EU cloud server.

Why EU Data Residency Matters for AI Inference

Every prompt sent to an AI model is potentially personal data under GDPR - it may contain user names, email contents, medical queries, or financial details. If inference happens on a US-hosted API, that data leaves EU jurisdiction. Running inference on a DCXV EU cloud server keeps all prompts and completions within EU borders, satisfying data residency requirements without relying on standard contractual clauses.

Beyond compliance, EU-hosted inference eliminates transatlantic round-trip latency. A model served from Prague or Frankfurt responds 80-120ms faster per request than the same model served from a US endpoint - a meaningful difference for interactive applications like chatbots and copilots.

GPU vs CPU Inference: When to Use Each

The right compute depends on model size and throughput requirements:

• CPU inference works well for small models (under 7B parameters at INT8/INT4), embedding models, and low-throughput use cases (under 20 requests/s). Modern CPUs with AVX-512 can run 7B models at 15-30 tokens/s - adequate for background processing or internal tools.
• GPU inference is necessary for large models (13B+ parameters), real-time interactive use cases, or batch workloads requiring 50+ tokens/s. A single RTX 4090 or A100 delivers 10-20x the token throughput of CPU inference for the same model.

Minimum Specs for AI Inference

CPU-only inference:

• Small (embedding models, classifiers) - 8 vCPU, 16 GB RAM, 100 GB NVMe SSD
• Medium (7B model, internal API) - 16 vCPU, 32 GB RAM, 200 GB NVMe SSD
• Large (13B model at INT4, moderate traffic) - 32 vCPU, 64 GB RAM, 500 GB NVMe SSD

GPU inference:

• Entry (7B-13B models, RTX 4090 24 GB VRAM) - 8 vCPU, 32 GB RAM, 24 GB GPU, 500 GB NVMe
• Production (34B-70B models, A100 80 GB VRAM) - 16 vCPU, 128 GB RAM, 80 GB GPU, 1 TB NVMe
• Multi-GPU (70B+ at full precision) - 2x or 4x A100/H100, 256+ GB RAM, 2+ TB NVMe

Recommended DCXV Configuration

DCXV cloud servers support both CPU-optimized and GPU configurations for AI inference workloads:

• 16 vCPU, 64 GB RAM, 500 GB NVMe - CPU inference for 7B-13B quantized models, internal tools
• GPU server with 24 GB VRAM - real-time inference for 7B-13B models, chatbot APIs
• GPU server with 80 GB VRAM - production inference for 34B-70B models

All configurations run on Tier III certified EU data centers with private networking for secure inference endpoints. Contact sales@dcxv.com to discuss GPU availability and multi-GPU configurations.

Quick Setup Commands

# Install Ollama for easy CPU/GPU model serving on Ubuntu 22.04
curl -fsSL https://ollama.com/install.sh | sh

# Start the Ollama service
sudo systemctl start ollama
sudo systemctl enable ollama

# Pull and run a model (Llama 3.1 8B as example)
ollama pull llama3.1:8b

# Test inference locally
ollama run llama3.1:8b "Explain EU GDPR data residency in one paragraph"

# Expose Ollama as an API on your private network
# Edit /etc/systemd/system/ollama.service - add Environment line:
# Environment="OLLAMA_HOST=0.0.0.0:11434"

sudo systemctl daemon-reload
sudo systemctl restart ollama

# Test the REST API from your application server
curl http://10.0.0.5:11434/api/generate 
  -d '{"model": "llama3.1:8b", "prompt": "What is GDPR?", "stream": false}'

# For higher-throughput production serving, use vLLM (GPU required)
pip install vllm

# Serve a model with OpenAI-compatible API
python -m vllm.entrypoints.openai.api_server 
  --model meta-llama/Llama-3.1-8B-Instruct 
  --host 10.0.0.5 
  --port 8000 
  --max-model-len 4096 
  --gpu-memory-utilization 0.85

# For CPU-only inference with llama.cpp
# Install build dependencies
sudo apt install -y build-essential cmake

git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp && cmake -B build -DLLAMA_AVX512=ON && cmake --build build -j $(nproc)

# Download a GGUF model (4-bit quantized 8B example)
# Place in models/ directory, then serve:
./build/bin/llama-server 
  --model models/llama-3.1-8b-instruct-q4_k_m.gguf 
  --host 10.0.0.5 
  --port 8080 
  --ctx-size 4096 
  --threads $(nproc)

Expected Performance Benchmarks

CPU inference (16 vCPU DCXV server, llama.cpp, INT4):

• Llama 3.1 8B at Q4_K_M - 18-28 tokens/s generation
• Embedding model (nomic-embed-text) - 200-400 embeddings/s
• Latency to first token (8B model) - 800ms-2s

GPU inference (RTX 4090 24 GB, vLLM):

• Llama 3.1 8B at FP16 - 80-120 tokens/s per request
• Llama 3.1 8B throughput (batched) - 400-800 tokens/s total
• Latency to first token - 150-400ms

GPU inference (A100 80 GB, vLLM):

• Llama 3.1 70B at FP16 - 25-45 tokens/s per request
• Mistral 7B throughput (batched) - 1,200-2,000 tokens/s total

Bottom Line

AI inference in Europe is a GDPR requirement for any application processing personal data through LLMs or other AI models. CPU inference on a well-provisioned DCXV server handles internal tools and low-traffic APIs. GPU inference is the right choice for interactive end-user applications. Both options keep your inference traffic on EU infrastructure under EU jurisdiction. Reach out to sales@dcxv.com to configure the right setup for your model size and throughput target.