The Blueprint for Scalable AI

Overcoming AI Inference Bottlenecks

As AI moves from experimentation to enterprise-scale deployment, the challenge has shifted from model training to delivering inference at speed and scale. Yet many organizations face a critical—and often overlooked—obstacle: data bottlenecks that undermine performance and ROI.

Despite the power of today’s GPUs, up to 70% of their capacity can sit idle, waiting for data. The result? Latency, frustrated users, and rising infrastructure costs.

At this exclusive executive roundtable, we’ll explore how to overcome these infrastructure barriers and unlock real-time AI performance. Discover how high-performance storage and low-latency I/O are reshaping the AI stack—ensuring your systems deliver insights instantly, efficiently, and at scale.

Join us in London on September 24th for a private gathering of AI and technology leaders. Together with experts from WEKA and NVIDIA, we’ll explore how the next-gen AI stack is being reimagined—from accelerating KV cache performance to enabling real-time agents and ultra-large model inference. Learn how the Blackwell-powered NVIDIA GB200 platform, combined with the WEKA Data Platform, is delivering blazing-fast, token-optimized AI across enterprise environments.

Why attend?

Learn how to efficiently scale AI workloads with NVIDIA GB200 and the WEKA Data Platform.
Understand why KV cache offload to ultra-low-latency storage is key to accelerating inference.
Explore token-optimized architectures that cut infrastructure costs without sacrificing speed.
Discover how to eliminate I/O bottlenecks and drive GPU utilization to 90% and beyond.

This exclusive roundtable offers a rare opportunity to rethink your AI strategy and future-proof your infrastructure. Join us in the heart of London for a high-level discussion on building the foundation for the next era of AI.

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Valentin (Val) Bercovici is WEKA's Chief AI Officer. He has extensive experience in the data infrastructure industry, having previously been the Chief Technology Officer at NetApp/SolidFire, where he drove innovation in cloud storage and data management solutions. Val co-authored the Windows Shadowcopy snapshots and has made significant contributions to the storage standards community. As Co-chair of the Storage Networking Industry Association's (SNIA) Solid State Storage Initiative, Val helped to establish the first NAND Flash SSD storage standards. Additionally, Val served as the Chair of the SNIA Cloud Storage Initiative (CSI), where he led the development of the international S3 standard CDMI (ISO 17826). He was also a founding member of the Kubernetes Cloud Native Computing Foundation’s Governing Board, helping to shape the global direction of container orchestration. Val holds patents in AI agent smart contracts, streaming data integrity, and augmented reality (AR) for data center maintenance. His work continues to push the boundaries of what’s possible at the intersection of AI, cloud, and emerging technologies.

Chief AI Officer

Val Bercovici

WEKA

Dr. Adam Grzywaczewski is a senior deep learning data scientist at NVIDIA, where his primary responsibility is to support customers in scaling and deployment of their deep learning workloads. Over more than a decade, Adam has specialized in large-scale DNN training, focusing not just on deep learning system and software design, but also on algorithms that allow for large batch data/model/pipeline parallel training. Adam works with customers with high computational needs, including key automotive customers and organizations with the need for large scale NLP and conversational AI. He also leads an EMEA AI team that partners with NVIDIA’s strategic customers to address the challenges of building and deploying deep learning models, while serving as a key interface to NVIDIA Engineering, Product, and Research groups. Adam also focuses on techniques for computationally efficient inference, including parameter/compute efficient model design and model pruning and quantization. Prior to joining NVIDIA, Adam was responsible for building up the UK government’s machine-learning capabilities while at Capgemini, and worked in the Jaguar Land Rover Research Centre, where he worked on the self-learning car project.