Name: Supermicro Open Storage Summit 2026
Start: 2026-08-11T17:00:00.000Z
End: 2026-09-03T18:00:00.000Z

Agenda

Optimizing AI Inference Performance and Cost with Multi-Tier Storage

AI inference storage requires a tradeoff between performance and cost. Achieving the targeted performance at the budgeted cost requires careful engineering and selecting the best storage technology for each task. Combining a high-performance parallel file system using all-flash media with an object storage tier based primarily on HDDs provides both high performance and optimized TCO. This session brings together leaders in file and object storage, flash media, HDDs and CPUs from WEKA, Scality, Samsung Semiconductor, WD and Supermicro to describe how it all comes together in an engineered solution.

Vertical AI Solutions

While they typically use common AI hardware and software, vertical industries such as financial services, life sciences/pharma and manufacturing have unique requirements, including response times, data and workflows. In this panel with DDN, Kioxia, WD and Supermicro speakers examine financial market exchanges and genomics workflows as examples of how the software, SSD and HDD storage media, as well as system requirements, are addressed and where commonalities and differences emerge.

Moving AI from POC to Production Like all IT initiatives, AI projects require user acceptance testing, integration testing and deployment. However, they also face additional hurdles before reaching production, including cost and token economics, the selection of scalable infrastructure, data readiness, access and governance, and user onboarding. This panel with Nutanix, MinIO, PEAK:AIO and Supermicro discusses these challenges and offers best practices based on past deployments.

Infrastructure for Neocloud AI Neoclouds, which specialize in large-scale AI workloads, offer the scale of hyperscalers while maintaining an AI-specific focus. This session focuses on how neoclouds provide differentiated services, scale and storage to support customers' AI workloads. Representatives from Crusoe discuss how the neocloud differentiates its features and functionality alongside partners Supermicro, VAST Data and Kioxia.

Architecting AI Inference and Data Management for Maximum Scale Inference at scale requires optimization of compute, storage and networking performance. In this session, speakers discuss how IBM, Supermicro, and Kioxia can provide a highly scalable solution for AI inference. The session also explains why KV Cache and AI Data Platform (AIDP) are critical for improving inference response time and optimizing the design of an AI Factory. And the benefits that the combine solution brings for improving time-to-first-token responses and optimizing the data and compute design of AIDP on Supermicro all-flash storage systems.

Enterprise Storage Modernization for AI Enterprises often have a variety of siloed legacy storage systems. Adding new capacity involves extending these legacy systems or deploying greenfield infrastructure. AI introduces new requirements for incorporating existing storage systems and data while adding purpose-built capacity. This session features Hammerspace, Sandisk and Supermicro discussing how to integrate existing and new capacity with emerging AI workflows.

Breaking the Context Wall: Storage for Scalable Agentic AI Agentic AI at scale will require high-capacity, low-latency context memory to store and retrieve key value, or KV, cache data used in the inference workflow. New storage tiers and architectures have been proposed to solve this problem by balancing the reprocessing of inference queries against the retrieval of previously computed tokens. This new tier, positioned between the local GPU system SSDs and network storage, has been called “context memory” and allows long context tokens to persist in a large-scale storage array. This session with VAST Data, Solidigm and Supermicro describes the implementation, uses and trade-offs of the context memory tier.

Enabling AI with Data Lakes and Lakehouses Data lakes and lakehouses have found a new purpose in AI beyond traditional analytics and data warehousing. Modern data lakes are built on object storage infrastructure and use industry-standard data formats to ensure interoperability. The transactional infrastructure capabilities of data lakehouses also support AI workflows natively, including data pipeline processing, inference workloads and context memory serving. Speakers from MinIO, AMD and Supermicro discuss the changing roles of data lakes and lakehouses.

AI Data Platforms for Enterprise AI AI data platforms represent a new category of AI infrastructure that handles the pre-processing of enterprise data, including data ingestion, normalization, vectorization and the hand-off of data to the enterprise AI factory. These scalable systems, which incorporate both data management and GPU infrastructure, can start small for workgroups and scale as demand grows. Preintegrated software and hardware enable faster on-site deployment and operational readiness. Built-in workflows for RAG, visual search and document processing, among other capabilities, provide an accessible starting point for AI. Experts from DDN, Solidigm and Supermicro discuss AI data platform use cases.

Edge-Cloud Storage-as-a-Service for CSPs and Enterprise The next generation of cloud storage incorporates on-premises edge services that provide a low-latency local storage node that can be centrally managed by the CSP. In this session, CSP Iron Mountain describes the next phase of Iron Cloud, including an on-prem solution for local backup and retrieval that also moves data to the Iron Cloud backend for long-term retention. This solution incorporates Scality object storage and uses Supermicro backend and edge systems.

Managing Unstructured Data for AI Unstructured data accounts for 80%-90% of an enterprise’s unique data. Most enterprises retain only 5% of the data they generate due to cost and the rapid expansion of AI workflows, IoT sensors and other machine-generated data. Much of this data is valuable for future training, as well as compliance audits, debugging and understanding AI outputs. Aggregating, normalizing and processing this data form the foundation of enterprise AI implementation. Leaders from Cloudian, Hammerspace, Seagate and Supermicro discuss best practices for managing and retaining this data.

Enterprise AI Implementation Challenges and Solutions Enterprise AI, especially agentic AI, introduces new IT challenges, including data readiness and governance, hardware and software infrastructure, deployment models, scaling and economics, and operations. This session with Nutanix, AMD and Supermicro brings together experts in AI infrastructure from solution, silicon and systems perspectives to discuss on-prem and hybrid deployment models, solution integration and more.

Speakers

HOST

Rob Strechay

Dir./Principal Analyst & Host

theCUBE Research

Albert Tan

Staff Solution Architect

Supermicro

Allen Liu

Sr. Product Manager, Architecture solutions

Supermicro

Ben Lee

Director, Solution Management

Supermicro

Junxia Zhou

Senior Product Manager

Supermicro

Paul McLeod

Product Director, Storage Systems

Supermicro

Sherry Lin

Product Manager (Storage Solution)

Supermicro

William Li

GM, Solution Management

Supermicro

Michael Ang

Director, Storage Solutions

Supermicro

Wendell Wenjen

Commissioner, Salary Setting Commission

Supermicro

Vince Chen

Director of Solutions Architecture

Supermicro

Nicola Tan

Director of Market Development, Enterprise AI

AMD

Varun Selvaraj

Business Development Manager - Enterprise AI

AMD

Peter Sjoberg

VP - Worldwide Solution Architects

Cloudian Inc

Omar Lari

Senior Product Leader

Crusoe

Andrew Murphy

Sr. Director Product Management

DDN

Moiz Kohari

VP Enterprise AI

DDN

Molly Presley

SVP Global Marketing

Hammerspace

Ka Wai Leung

AI Solutions Product Management

IBM

Angela Gill

Director, Partner Enablement

Intel

Gary Brown

Sr Product Marketing PM, Xeon Products

Intel

Cliff Madru

VP, GM Global Iron Cloud and Data Services

Iron Mountain

Anders Graham

Sr. Director | Business Development | Solid State Drives for Cloud/Hyperscale, Data Centers, PCs

KIOXIA America, Inc.

Bill Miller

Sr. Dir, Lakehouse & AI Solutions

MinIO

Greg DeMichillie

VP Product Marketing

MinIO

Mayank Gupta

Director for AI & Cloud Native Solutions Marketing

Nutanix

Ruhi Sehgal

Agentic AI Solutions Marketing

Nutanix

Chris Ratcliffe

VP Marketing

PEAK:AIO

Jonathan Prout

Director, Memory Business Development

Samsung Semiconductor

Praveen Midha

Director Segment & Technical Marketing, Data Center Flash at SanDisk

Sandisk

Greg DiFraia

Senior Vice President, AI Alliances & Partnerships

Scality

Mohamad El-Batal

Chief Systems Technologist - Office of the CTO

Seagate

Scott Shadley

Director, Storage Planning and Pathfinding

Solidigm

Pompey Nagra

Product and Ecosystem Marketing

Solidigm

Phil Manez

VP, Strategic Initiatives

VAST Data

Anat Heilper

Director of AI Architecture

VAST Data

Anthony Lembo

VP, Global Systems Engineering

WEKA

Marc Tanguay

Sr. Product Marketing Manager

Brad Warbiany

Director, Product Marketing