Uploaded November 2025 | Updated September 2026, 2 weeks ago
The rate of change in the structure and capabilities of applications has never been as high as in the last year. There's a huge shift from stockpiling data cheaply to leveraging data to create insight with GenAI and to capitalize on business leads with predictive AI. Excitement and opinions about where storage matters run rampant. Thankfully, we can "follow the data" to pinpoint whether storage performance is critical in the compute node or just in the back end, discern the relative importance of bandwidth and latency, determine whether the volume and granularity of accesses is suitable for a GPU, and what the range of granularities of accesses are. Walking through recent developments in AI apps and their implications will lead to insights that are likely to surprise the audience.
There are new opportunities to create innovative solutions to these challenges. The architectures of NAND and their controllers may adjust to curtail ballooning power with more efficient data transfers and error checking. IOPs optimizations that will be broadly mandatory in the future may be pulled in to benefit some applications now. New hardware/software codesigns may lead to protocol changes, and trade-offs in which computing agents and data structures are best suited to accomplish new goals. Novel software interfaces and infrastructure enable movement, access, and management of data that is tailored to the specific needs of each application. Come join in a fun, refreshing, provocative, and interactive session on storage implications for this new generation of AI applications!
AI applications are changing fast and so are their storage implications Build platforms for insight through GenAI and predictive AI on top of data storage Careful application analysis yields clarity on storage criticality, which metrics are important, what kind of computing elements are needed, and what storage access characteristics are The whole vertical stack is in play: NAND, controllers, interfaces, buffering, policies, and software abstractions. Every layer of hardware and software is affected.
Presented by CJ Newburn, NVIDIA and Wen-Mei Hwu, NVIDIA
Learn More:
• SDC Website: snia.org/sniadeveloper
• SNIA Website: snia.org
• SNIA Educational Library: snia.org/library
• X: twitter.com/SNIA
• LinkedIn: linkedin.com/company/snia
The rate of change in the structure and capabilities of applications has never been as high as in the last year. There's a huge shift from stockpiling data cheaply to leveraging data to create insight with GenAI and to capitalize on business leads with predictive AI. Excitement and opinions about where storage matters run rampant. Thankfully, we can "follow the data" to pinpoint whether storage performance is critical in the compute node or just in the back end, discern the relative importance of bandwidth and latency, determine whether the volume and granularity of accesses is suitable for a GPU, and what the range of granularities of accesses are. Walking through recent developments in AI apps and their implications will lead to insights that are likely to surprise the audience.
There are new opportunities to create innovative solutions to these challenges. The architectures of NAND and their controllers may adjust to curtail ballooning power with more efficient data transfers and error checking. IOPs optimizations that will be broadly mandatory in the future may be pulled in to benefit some applications now. New hardware/software codesigns may lead to protocol changes, and trade-offs in which computing agents and data structures are best suited to accomplish new goals. Novel software interfaces and infrastructure enable movement, access, and management of data that is tailored to the specific needs of each application. Come join in a fun, refreshing, provocative, and interactive session on storage implications for this new generation of AI applications!
AI applications are changing fast and so are their storage implications Build platforms for insight through GenAI and predictive AI on top of data storage Careful application analysis yields clarity on storage criticality, which metrics are important, what kind of computing elements are needed, and what storage access characteristics are The whole vertical stack is in play: NAND, controllers, interfaces, buffering, policies, and software abstractions. Every layer of hardware and software is affected.
Presented by CJ Newburn, NVIDIA and Wen-Mei Hwu, NVIDIA
Learn More:
• SDC Website: snia.org/sniadeveloper
• SNIA Website: snia.org
• SNIA Educational Library: snia.org/library
• X: twitter.com/SNIA
• LinkedIn: linkedin.com/company/snia
