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Gen6 is coming, but what is Needed from NV Storage?

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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.

Emerging Trends in Automotive Fabrics and Data Security

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The blurring of the lines between data centers and automobiles continues to grow fuzzier.   This talk explores the trends in automotive fabrics tying together a wild array of sensors, displays, processors, memory, and storage.  Another data center trend that may actually appear first in cars is the need for post-quantum security algorithms, preventing malicious intruders from steering our cars off bridges.

Modern Data Management at Exabyte Scale — With Visibility, Efficiency, and Control

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As cloud adoption accelerates, organizations are increasingly managing data estates that span petabytes and exabytes. At this scale, traditional tools fall short. Modern data management in the cloud must go beyond just storage and embrace granular visibility, governance, and optimization. This session explores how cloud platforms are evolving to meet these demands with scalable, intelligent solutions.

Assessing AI Storage Communication Performance At Scale

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How do we assess the performance of AI network and storage infrastructure that is critical to the successful deployment of today's complex AI training and inferencing engines? And is it possible to do this without needing to provision racks of expensive GPU Capex? This presentation discusses methodologies and considerations in performing such assessments. We look at different topologies, host and network side considerations and metrics. The performance aspects of NICs/SmartNICs, storage offload processing, switches and interconnects are examined.

Advancing the AI Factory Sustainability

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ChatGPT began AI's watershed moment that triggered IT infrastructure's tectonic shift and race in extraordinary and lasting commitments to AI Factory. Many governments and enterprises alike are making enormous capital and people investments to not be left behind the AI boom. Corporate boardrooms are evaluating purposeful infrastructure plans. What is the best architectural decision - retrofitting, built from scratch or adopt a wait-and-see? This fork in the road has given pause and decision paralysis to some infrastructure decision makers.

Simulating CXL.mem for Fun and Profit

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CXL.mem enables hosts to expand their memories beyond individual servers and access memory regions using load and store instructions. In addition, CXL.mem enables memory sharing among its endpoints. Realizing memory sharing requires extending the coherency management protocol beyond individual hosts. Hosts and devices need to track the state of each memory region using individual finite state machines.

Highly Scalable, Masterless, Distributed Filesystem at Rubrik

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Rubrik is a cybersecurity company protecting mission critical data for thousands of customers across the globe including banks, hospitals, and government agencies. SDFS is the filesystem that powers the data path and makes this possible. In this talk, we will discuss challenges in building a masterless distributed filesystem with support for data resilience, strong data integrity, and high performance which can run across a wide spectrum of hardware configurations including cloud platforms.

Cloud Storage Considerations for Retrieval Augmented Generation (RAG) in AI Applications

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Data enhances foundational LLMs (e.g. GPT-4, Mistral Large and Llama 2) for context-aware outputs. In this session, we'll cover using unstructured, multi-modal data (e.g. PDFs, images or videos) in retrieval augmented generation (RAG) systems and learn about how cloud object storage can be an ideal file system for LLM-based applications that transform and use of domain-specific data, store user context and much more.

Disrupting the GPU Hegemony: Can Smart Memory and Storage Redefine AI Infrastructure

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AI infrastructure is dominated by GPUs — but should it be? As foundational model inference scales, performance bottlenecks are shifting away from compute and toward memory and I/O. HBM sits underutilized, KVCache explodes, and model transfer times dominate pipeline latency. Meanwhile, compression, CXL fabrics, computational memory, and SmartNIC-enabled storage are emerging as powerful levers to close the tokens-per-second-per-watt gap.

Rethinking Storage for the AI/ML Era: Disaggregation Powered with FDP

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Generative AI models, such as Stable Diffusion, have revolutionized the field of AI by enabling the generation of images from textual prompts. These models impose significant computational, and storage demands in HPC environments. The I/O workload generated during image generation is a critical factor affecting overall performance and scalability. This paper presents a detailed analysis of the I/O workload generated by Stable Diffusion when accessing storage devices, specifically NVMe-oF drives.
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