AI at Scale
From AI Pilot to Enterprise Production
NVIDIA GTC Berlin 2026 Side Event
20 October 2026 | 13:30–16:00 | JW Marriott Berlin
Moving AI from pilot projects into production requires more than powerful GPUs. Data architecture, compute, cooling, storage and financing all need to scale together.
AI at Scale brings together industry experts to discuss how enterprise AI infrastructure can be built for performance, efficiency and long-term growth.
Join us for an exclusive Executive Lunch & Learn with expert sessions, networking and open discussion in Berlin.
A scalable AI environment depends on more than one technology layer.
The event follows four key stages of enterprise AI infrastructure:
- ARCHITECT – Build the right data foundation
- BUILD – Turn compute, power and cooling into scalable infrastructure
- OPERATE – Keep data flowing and systems productive
- SCALE – Structure investment for long-term growth
Cloudian
Feeding the AI Factory: The Data Architecture Behind Every GPU
An AI Factory is only as productive as the data pipeline feeding it. The organizations that win the next phase of enterprise AI won’t just have the fastest GPUs — they’ll have the data architecture that keeps those GPUs running at full utilization, securely and sovereignly, from pilot to production scale.
Focus:
Data architecture · Scalability · AI readiness · Data sovereignty
Boston
Your Next AI Bottleneck Is Not the GPU
Speaker: Daniel Bender, Boston
The GPU remains at the heart of the AI Factory. However, its success is determined not only by the amount of compute power installed, but by how reliably and consistently that compute capacity can be utilised. The winners of the next phase of AI infrastructure will therefore not simply deploy the fastest chips, but will master compute, power and cooling as one integrated system.
Focus:
GPU density · Compute · Power · Cooling · TCO
Western Digital
AI Is a Data System: The Economics Behind AI at Scale
Speaker: Alex Sageda, WD
For the past three years, the AI industry has focused on generating intelligence through ever-larger models and compute infrastructure. The next three years will be about managing, storing, and monetising the data AI creates. As organisations move from pilots to production, they are discovering that inference is not just a compute workload but a data creation engine. This session explores how inference data, KV cache and hybrid flash-HDD architectures are reshaping AI economics, and why data-centric infrastructure is becoming essential to reducing cost per token at scale.
Focus:
Performance · Capacity · Data movement · Storage tiering · Data lifecycle
LCS
From AI Ambition to Investment Reality
Speaker: Jörg Schilling, LCS
Flexibility in technical operations and scalable AI infrastructures place high demands not only on the technical solution but also on the financing and the interaction between these two aspects.
Focus:
Infrastructure financing · Investment flexibility · Scaling · Lifecyc
Register for AI at Scale
AI infrastructure does not become scalable simply by adding more GPUs.
The data foundation, compute architecture, storage, cooling, power and investment model all need to evolve together.
Join Boston and industry experts in Berlin for an afternoon focused on the practical decisions behind moving from AI pilot to enterprise production.
