Technical and practical content on private AI, local infrastructure and regulatory compliance for enterprises and public organisations.
FUNDAMENTALS
What is a Micro Data Center for Artificial Intelligence?
A micro AI data center is a compact infrastructure — one or several high-performance servers — designed to run language models and AI entirely on-premise within an organisation's facilities, without relying on external cloud services.
Unlike a traditional data centre, it is optimised for AI inference workloads: using low-power FPGA accelerators (such as AMD Alveo), large amounts of RAM and ultra-fast NVMe storage to load and run models like Llama 3 or Mistral with millisecond latency.
The result is AI capability equivalent to cloud services like ChatGPT Enterprise or Azure OpenAI, but with data processed 100% within the corporate perimeter, no per-token costs and full sovereignty over models and data.
→ FlightLLM (ACM FPGA'24) · arXiv:2401.03868
COMPARISON
Private AI vs Cloud AI: advantages for enterprises
Cloud AI services (OpenAI, Azure, Google Vertex) offer immediate availability, but present three structural problems for organisations handling sensitive data: unpredictable variable costs, data transmission to external servers and total vendor dependency.
Private on-premise AI inverts the model: fixed predictable cost, data that never leaves the internal network and the ability to train the model on the organisation's own data. For a law firm, clinic or industrial company, the difference is critical: the model is not only more secure, it is more accurate because it knows the internal terminology and processes.
Organisations that have adopted local AI typically report operational cost reductions of 40–70% compared to equivalent cloud plans over an 18–24 month horizon.
→ MDPI Electronics 15(5), 2026 · doi:10.3390/electronics15051052
LEGAL · GDPR
How to achieve GDPR compliance using local AI
The General Data Protection Regulation (GDPR) imposes explicit restrictions on the transfer of personal data to third countries and the assignment to external data processors. When a company sends data to a cloud AI API, that provider becomes a data processor and a DPA must be signed.
With local AI, data never leaves the internal network: no international transfer, no external processor and no risk of the provider using the data to retrain their models. This radically simplifies compliance, especially in healthcare, legal services and public administration, where GDPR is applied most rigorously.
Our architecture includes TLS 1.3 across all internal communications, role-based access control and complete audit logs compatible with leading security standards.
→ GDPR Art. 28 · ISO/IEC 27001 · ENS RD 311/2022
TECHNICAL · LLM
Llama 3 on private enterprise infrastructure
Meta Llama 3 (8B and 70B parameters) is the reference open-source model for private enterprise deployments. Its licence permits commercial use, modification and fine-tuning with proprietary data, making it the ideal foundation for building sector-specific AI assistants.
On our Cisco UCS infrastructure with AMD Alveo FPGA, quantised Llama 3 8B INT4 runs with latencies below 200ms per token, consuming less than 150W — a fraction of an NVIDIA H100 GPU's power draw. The 70B model requires high-speed NVMe and 128 GB RAM, standard in our server configuration.
Sector fine-tuning uses LoRA/QLoRA techniques to adapt the base model with between 500 and 5,000 client documents, without retraining the full model, reducing personalisation time and cost by up to 90%.
→ Meta Llama 3 · arXiv:2407.21783 · LUT-LLM arXiv:2511.06174
FINANCE · ROI
Real cost of running AI in the cloud vs on-premise
A comparative analysis for a medium-sized company processing 500,000 tokens/day with GPT-4o (approx. €0.005/1K output tokens) implies a monthly spend of €2,500 — €30,000/year — excluding variable latency and price increase risk. Over 36 months: more than €90,000 in tokens.
An enterprise AI server on Cisco UCS with AMD Alveo FPGA has an initial cost of €15,000–25,000 and power consumption of ~300W (approx. €500/year). Over 36 months, total cost is below €27,000: a saving of over 70% with better privacy, lower latency and a model adapted to the business.
For sectors with high document volume — legal, healthcare, industrial — return on investment typically occurs between months 10 and 18, depending on usage volume and the labour costs that the AI assistant replaces or reduces.
→ OpenAI API pricing May 2025 · AMD Alveo U50 TDP: 75W