2026 West Coast IBM Data and AI Scale Storage New User Seminar
Why Attend
How are you managing the data behind your HPC and AI workloads today?
As GPU environments scale and LLM driven applications expand, data has become the primary bottleneck. Many organizations are dealing with fragmented pipelines, performance constraints, and challenges keeping AI models aligned with current data.
This one day, in person event is designed to help you rethink your AI data architecture and understand how to simplify data access, improve performance, and accelerate time to value.
What You Will Learn
Build a Modern AI Data Platform
Support HPC and AI workloads with high performance, scalable storage
Design a global data layer across on prem, cloud, and edge
Enable both model training and real time inferencing
Eliminate Data Bottlenecks in AI Pipelines
Why traditional and copy based architectures slow AI adoption
How to reduce complexity and improve throughput
RAG and LLM Integration with Content Aware Storage
Enable LLMs to access enterprise data directly where it resides
Eliminate the need to copy data into separate pipelines or vector databases
Keep AI outputs aligned with continuously changing data
NVIDIA Integrated AI Architectures
IBM and NVIDIA collaboration for modern AI infrastructure
Best practices for DGX and SuperPOD environments
Ensure storage keeps pace with GPU performance
Real World Insights
Lessons learned from AI deployments at scale
Practical guidance on where to start and what to prioritize
About IBM Storage Scale
IBM Storage Scale is purpose built for data intensive workloads including HPC, AI, and analytics. It provides high performance access to unstructured data, a global namespace, and the ability to scale across distributed environments.
With the addition of Content Aware Storage, IBM enables a new approach to AI data management, allowing models to access and continuously update against enterprise data in place, reducing complexity and improving accuracy.
Who Should Attend
IT and infrastructure leaders supporting AI or HPC workloads
Data architects and AI platform teams
Organizations exploring or scaling LLM and RAG use cases
A practical, technical session focused on how to remove data constraints and accelerate AI initiatives.