NVIDIA GTC 2025: Cloudian Share AI Storage Innovation

Cloudian, Inc. announced its participation at NVIDIA GTC 2025, featuring 2 technical sessions and demonstrations of its AI-optimized storage solutions.

The company’s technical experts will be available at booth 219 in the San Jose Convention Center, CA, throughout the conference, March 17-21, to discuss how enterprises can optimize their AI infrastructure with the company’s scalable storage platform.

Michael Tso, CEO and co-founder, Cloudian, will join Sunil Gupta, co-founder, CEO and MD, Yotta Data Services and Kevin Deierling, SVP, networking, NVIDIA Corp., to present Pioneering the Future of Data Platforms for the Era of Generative AI on Thursday, March 20, at 10:00 AM PDT. The session will explore how data management is transforming enterprise AI systems and how NVIDIA is collaborating with storage innovators to create accelerated data platforms optimized for GenAI workloads and real-time RAG pipelines.

Additionally, Peter Sjoberg, VP, WW solution architects, Cloudian, will participate in a panel discussion titled Storage Innovations for AI Workloads on Monday, March 17, at 4:00 PM PDT. Joining storage experts from NVIDIA, NetApp, DDN, VAST Data, and Dell, Sjoberg will discuss critical considerations for architecting AI-focused data centers and how organizations can maximize storage performance for AI applications.

“As AI transforms enterprise computing, organizations need storage infrastructure that can efficiently manage massive data volumes while delivering the performance needed for modern AI workloads,” said Jon Toor, CMO, Cloudian. “At GTC, we’ll demonstrate how Cloudian’s integration with NVIDIA technology enables organizations to build scalable, high-performance infrastructure for both training and inference applications.”

Throughout conference, Cloudian technical experts will be available at the company’s booth to discuss:

• Storage architecture optimization for AI workloads
• Integration with NVIDIA GPUDirect Storage
• Scalable infrastructure for training and inference
• Data management for real-time RAG applications
• Cost-effective approaches to AI storage