On-Premise AI with Anyway Systems using distributed LLM Inference

Most organizations are already using AI, but often at the cost of data privacy, regulatory exposure, and vendor lock-in. Relying on external AI services means sensitive business data leaves the organization, creating risks that many executives underestimate. While on-premise AI promises full control and full privacy, it is commonly seen as too complex and expensive for SMEs.

This workshop shows why that is no longer true.

Anyway Systems, a spin-off from EPFL’s Distributed Computing Laboratory, demonstrates how organizations can run state-of-the-art AI models entirely on their own infrastructure, without the most specialized hardware or large AI teams. By using software to combine standard commodity GPUs into efficient AI clusters, organizations can keep all data in-house, reduce hardware costs by up to 5 times, and extend equipment lifetime by 2 times, all under a predictable, fixed-cost model.

What you will learn:

• Where AI creates hidden privacy and compliance risks
• How private, on-premise AI is now achievable with standard hardware
• How to control AI costs while avoiding vendor lock-in

Agenda:

• Introduction: AI and Privacy Risk.
• Solution Overview: Private AI Without Enterprise Complexity
• Live Demo: Running Large Models on Commodity GPUs
• Q&A: Use Cases, Costs, and Adoption Paths