SNIA Developer Conference September 15-17, 2025 | Santa Clara, CA
Gen6 is coming, but what is Needed from NV Storage?
Stevens Creek
Wed Sep 17 | 11:35am
Abstract
The rapid advancement of AI is significantly increasing demands on compute, memory and the storage infrastructure. As NVMe storage evolves to meet these needs, it is experiencing a bifurcation in requirements. On one end, workloads such as model training, checkpointing, and key-value (KV) cache tiering are driving the need for line-rate saturating SSDs with near-GPU and HPC attachment. On the other end, the rise of multi-stage inference, synthetic data generation, and post-training optimization is fueling demand for dense, high-capacity disaggregated storage solutions — effectively displacing traditional rotating media in the nearline tier of the datacenter. This paper explores the architectural considerations across both ends of this spectrum, including Gen6 performance, indirection unit (IU) selection, power monitoring for energy efficiency, liquid cooled thermal design, and strategies for enabling high capacity through form factor and packaging choices. We demonstrate how thoughtful design decisions can unlock the full potential of storage systems in addressing the evolving challenges of AI workloads.
Learning Objectives
What aspects of AI applications and workloads are driving the divergence in storage requirements? What are important architectural requirements for Near GPU SSDs? With the increasing power demands accompanying Gen6 performance, what are the different ways to manage energy efficiency on SSDs?
Related Sessions
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Bug detection and triaging in complex storage systems pose unique challenges that distinguish them from general-purpose or SaaS-based software. Unlike conventional code which largely operates in a straightforward user space, storage solutions must seamlessly integrate with the operating system kernel, device drivers, and underlying hardware devices. This tight coupling introduces additional complexity in logging, concurrency, and operational flow. For instance, storage systems often span hundreds of threads and processes, each writing into shared log files without conventional transactional guarantees. Such intricate interactions make it difficult for existing AI-based bug-tracking solutions which are typically trained on general codebases to deliver effective results.
To address these limitations, we propose a novel approach that supplements the system code with knowledge extracted from high-level integration test cases. These tests, often written in human-readable scripting languages such as Python, capture end-to-end system behavior more effectively than narrowly focused unit tests. By converting the insights from integration tests into a structured knowledge graph, our methodology provides an AI bug-triaging agent with rich contextual understanding of system interactions, inter-process communications, and hardware events. This deeper, scenario-driven perspective empowers the agent to pinpoint and diagnose issues from storage system failures that would otherwise be hidden in the labyrinth of kernel-mode calls, user-mode processes, and low-level device drivers. Our early findings suggest that this targeted fusion of code analysis and integration-test-based knowledge significantly enhances both the speed and accuracy of bug identification in storage software an advancement poised to transform how complex system bugs are tracked and resolved.
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The extreme growth in modern AI-model training datasets, as well as the explosion of Gen-AI data output are both fueling unprecedented levels of data-storage capacity growth in the datacenters. Such rapid growth in mass-capacity is demanding evolutionary steps in foundational storage technologies to enable higher areal density, optimized data-access interface methodologies and highly efficiency power/cooling infrastructure. We will explore these evolutionary technologies and take a sneak peek at the future of mass data-storage in the AI datacenters.
Gen6 is coming, but what is Needed from NV Storage?
The rapid advancement of AI is significantly increasing demands on compute, memory and the storage infrastructure. As NVMe storage evolves to meet these needs, it is experiencing a bifurcation in requirements. On one end, workloads such as model training, checkpointing, and key-value (KV) cache tiering are driving the need for line-rate saturating SSDs with near-GPU and HPC attachment. On the other end, the rise of multi-stage inference, synthetic data generation, and post-training optimization is fueling demand for dense, high-capacity disaggregated storage solutions — effectively displacing traditional rotating media in the nearline tier of the datacenter. This paper explores the architectural considerations across both ends of this spectrum, including Gen6 performance, indirection unit (IU) selection, power monitoring for energy efficiency, liquid cooled thermal design, and strategies for enabling high capacity through form factor and packaging choices. We demonstrate how thoughtful design decisions can unlock the full potential of storage systems in addressing the evolving challenges of AI workloads.
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