Afleveringen
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On this episode of the Shared Everything podcast Nicole sits down with Dan Nishball, Ellie Holbrook, and Harley Blackard of SemiAnalysis to explore one of the most ambitious ideas in AI infrastructure: space datacenters. Using SemiAnalysis's orbital compute cost model and detailed report as a starting point, the conversation examines the real economics behind deploying AI infrastructure beyond Earth, including launch costs, cooling, hardware reliability, and power. Along the way, the group discusses the constraints already shaping AI growth on the ground, from semiconductor supply and grid access to labor, datacenter construction, and energy infrastructure. Rather than asking whether space datacenters are possible, the discussion focuses on a more practical question: under what conditions would they become economically rational?
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SciNet, one of Canada's primary academic supercomputing centers, recently deployed a new infrastructure platform built around AMD CPUs, H100 GPUs, NDR InfiniBand, and VAST Data. The deployment provides a useful look at how modern HPC environments are evolving as traditional simulation workloads increasingly share infrastructure with AI. In discussing the design, operation, and future direction of the system, CTO Daniel Gruner highlights the growing importance of data platforms that can simultaneously deliver performance, operational simplicity, security, and multi-tenant services across a diverse scientific computing environment.
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Zijn er afleveringen die ontbreken?
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In this episode of the Shared Everything podcast, Derrick Harris speaks with Oxford Nanopore VP of Machine Learning Mike Vella about the company’s relatively unique approach to doing AI in the fast-moving field of genomics. From hand-programming CUDA kernels to dealing with the gigabyte per second of data coming off its DNA sequencers, Vella explains how his team balances the promise of fast, inexpensive sequencing with the realities of edge computing.
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On this episode of the Shared Everything podcast from VAST, Nicole talks with Chris Powell, Quantum Information Science Lead at SAIC, about what “data” actually means in a quantum system. We move past storage and into representation, where problems have to be mapped into mathematical forms like Hamiltonian matrices and expressed directly into quantum hardware with no traditional memory or registers. We also delve into how hybrid architectures tie CPUs, GPUs, and QPUs together, why classical data infrastructure becomes more important rather than less so, and how quantum computing shifts the model from retrieving answers to shaping systems where answers emerge from probabilities.
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In this episode of the Shared Everything podcast, Nicole Hemsoth Prickett speaks with Norm Marks, VP of Automotive at NVIDIA, about how autonomous systems are evolving from detection to prediction and now reasoning-driven AI. Marks explains how that shift is driving massive increases in GPU scale, synthetic data generation, and simulation, forcing companies to rethink infrastructure as training pipelines expand across hybrid datacenter and cloud environments. The result is a new class of AI factories built to train and operate autonomy at industrial scale.
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In this episode of Shared Everything, we sit down with Jeff Denworth, co-founder of VAST Data, and Scott Shadley of Solidigm to unpack what’s actually driving the current flash supply crunch. The conversation moves from NAND physics and fab constraints to hyperscaler buying behavior and the sudden surge in AI-driven demand, explaining why this cycle is fundamentally different from past downturns. At the center is a clear reality check for anyone building AI infrastructure right now: flash has become a limiting resource, and software efficiency and architecture now determine how much usable capacity the industry really has.
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On today's episode of the Shared Everything podcast, Nicole is live at SC25 with Dan Stanzione, Executive Director of the Texas Advanced Computing Center (TACC), for a look at why Horizon required a fundamental architectural reset. Stanzione explains how rising GPU power densities, liquid cooled 20 megawatt racks, and an increasingly irregular IO profile forced TACC to abandon long held assumptions about parallel filesystems. Years of watching billions of tiny files, unpredictable 4k and 64k reads, and metadata stalls slow entire machines led them to an all solid state tier and a VAST global namespace built for resilience, consistency, and shared access at scale. He describes how this model simplifies AI and hybrid scientific workflows, why the file system has always been the real point of failure, and how Horizon’s architecture reflects a world where IO, not FLOPS, determines what large scale science can do next.
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In this episode Nicole talks to Jeff Denworth, Co-Founder of VAST Data, about the deep architectural shifts behind the 5.4 release, delving into how a new distributed runtime, native vector database, and event-driven compute layer transform VAST from a storage platform into a fully programmable AI operating system. Jeff explains how real-time vector inserts, parallelism without inter-node communication, and disaggregated shared-everything design make it possible to reason over data as it arrives, powering applications from Smart City analytics to trillion-scale AI pipelines.
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On today’s episode of the Shared Everything podcast, Nicole talks to Jason Vallery, who just joined VAST after a 13-year career at Microsoft where he helped build the Azure cloud from the ground up. Jason reflects on the early days of object storage and cloud-native computing, when scaling from petabytes to exabytes redefined what infrastructure meant, and explains how lessons from Azure’s hyperscale era now shape his vision for VAST’s role in the AI age. He talks about the convergence of file and object systems, the evolution of AI storage built for thousands of GPUs, and the industry’s pivot from “data gravity” to a world where compute follows power and data must follow compute. Together, they trace how public cloud principles birthed the AI supercomputers of today and how the next wave of disaggregated, multi-cloud “neo clouds” will demand architectures that look a lot like what VAST is building
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In this episode Nicole talks to Chris Powell, Chief Scientist at SAIC, and Kartik, Chief Scientist at VAST Data, about how the foundations of supercomputing are being rewritten by quantum advances, new architectures, and the collapse of distance between data and compute. Together they explore what happens when data becomes the environment of computation itself, how proximity and randomness define the next frontier, and why the systems of the future will think exactly where the information lives.
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In this episode Nicole talks to Laurent Sifre, co-founder and CTO of H Company and former DeepMind scientist behind AlphaGo, AlphaFold, and Chinchilla. They explore how his discoveries in model scaling shaped the design of H Company’s computer-use agents, including the Surfer H platform that learns to navigate software through perception and action instead of APIs. The conversation dives into data infrastructure, distributed KV caching, sovereign compute, and why the future of automation depends on smaller, specialized models that act in the real digital world.
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In this episode of the Shared Everything, reasoning models take center stage. No longer just text predictors, they now loop, branch, and drag in outside data, which blows open context windows and GPU limits. Alon Horev, CTO of VAST Data, unpacks how this shift strains infrastructure, while Kevin Deierling, SVP of Networking at NVIDIA, explains how NVIDIA Dynamo moves KV caches and workloads across GPUs, networks, and storage to keep agentic workflows moving. Data platforms become an extension of memory, enabling longer chains of thought, real-time agents, and secure, observable data paths. The result is a vivid picture of the AI datacenter as the nervous system for reasoning at scale.
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On this episode, Nicole sits down with Danny McGinniss, VP of Product Management for Cisco Compute, Jacob Liberman, Director of Enterprise Product at NVIDIA, and John Mao, VP of Business Development and Alliances at VAST, to pull apart what it really means when three of the biggest forces in infrastructure line up behind the Cisco Secure AI Factory with NVIDIA, an architecture that brings together Cisco’s compute and networking, NVIDIA AI Data Platform, and VAST InsightEngine. The episode walks through the reimagining the datacenter as an AI factory where security, storage, speed, and data gravity collide to make enterprise AI real.
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In this episode, Nicole speaks with Aaron Chaisson and Blake Golliher of VAST Data about how the company is reframing its mission for the AI era, centering on the idea of becoming the Operating System for AI. Aaron lays out the strategy behind this shift, while Blake—drawing on his deep background in building large-scale data platforms explains how the VAST SyncEngine enables customers to move and manage massive volumes of data across sites, clouds, and AI pipelines in real time. The discussion highlights why the ability to synchronize data at scale is critical for enterprise AI adoption, and how VAST’s approach marries technical architecture with business strategy to help organizations operationalize intelligence in ways traditional storage platforms never could.
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In this episode, we explore how SK Telecom, South Korea’s largest wireless carrier and now a major force in AI infrastructure, joined forces with VAST Data to tackle one of the most difficult problems in large-scale computing: building a sovereign AI cloud that doesn’t compromise on speed, security, or scalability. Facing the nation’s mandate to keep AI models, data, and infrastructure fully under domestic control, SK Telecom had to rethink GPU virtualization from the ground up. The result is a platform that delivers near-bare-metal performance, strict multi-tenancy, and instant provisioning, setting a new standard for how sovereign AI infrastructure can be designed and operated.
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In this episode, Nicole talks with Ken Patchett, VP of Datacenter Infrastructure at Lambda, about how hyperscale AI and sovereign LLMs are redefining datacenter and data management strategies. Ken highlights the challenge of data gravity, emphasizing the critical role of co-locating extensive storage infrastructure alongside ultra-high-density compute to support increasingly data-hungry workloads. He outlines Lambda’s "aggregated edge" model, designed for regional deployment of inference and enterprise workloads, enabling localized data processing and compliance with global sovereignty and privacy regulations. The conversation also addresses how these changes demand adaptive multi-density infrastructure, integrating flexible compute-storage designs that accommodate shifting hardware requirements and evolving regulatory landscapes.
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Stacks of federal reports tell countless stories of IT investments gone sideways, yet the stakes have never been higher as artificial intelligence reshapes government. David Hinchman, Director of IT and Cybersecurity at the Government Accountability Office (GAO), joins Shared Everything to dissect why federal technology initiatives often falter and how these invisible fault lines could dangerously widen in the age of AI. From planning pitfalls to hidden infrastructure challenges, Hinchman reveals the critical decisions that determine whether AI becomes government’s greatest tool...or its most costly failure.
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In this episode of Shared Everything, Glenn Lockwood, just named Principal Technical Strategist at VAST Data, shares what decades at the bleeding edge of large-scale systems design have taught him about architecting for an AI future that refuses to stay put. From building the first all-NVMe 30PB Lustre file system to designing Azure’s training clusters, Glenn walks us through why performance alone is no longer enough, why inferencing shattered traditional supercomputing data patterns, and why today’s infrastructure decisions must be guided not by legacy conservatism, but by intrinsic flexibility. With characteristic clarity and conviction, Glenn lays out the case for treating adaptability as a first-class citizen in architecture...and why VAST is where he’s chosen to do just that.
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00:00 – 02:00
Intro and Kartik’s background in physics and industry evolution; early days of personalized medicine and genomic research.02:00 – 04:30
Breakdown of three main advances in life sciences: genomics, gene editing (CRISPR), and long-read sequencing technologies like Oxford Nanopore and PacBio.04:30 – 06:30
Deep technical dive into nanopore sequencing: how it works, why it matters, and why it requires GPU acceleration.06:30 – 08:30
The computational bottleneck: memory mapping, random I/O, why short-read sequencers are now limiting, and why SSDs are necessary.08:30 – 10:00
Parallel file systems break under modern life sciences loads; shift toward storage architectures that can handle random I/O at scale.10:00 – 12:30
How AlphaFold reshaped structural biology and compute expectations; protein folding as a graph neural network challenge.12:30 – 15:00
LLMs in pharma, managing clinical trial data, and the rise of mixed, hybrid workloads in research computing.15:00 – 17:00
Microscopy at scale (cryo-EM, light sheet imaging) and the data tsunami—petabytes per microscope, per year.17:00 – 19:30
Shifting away from HPC-era assumptions: new workloads, new storage expectations, and lessons from vendors like Oxford Nanopore.19:30 – 20:36
What’s next: generative AI models trained on molecular sequences and protein structures; a vision of disease-free future. -
In this episode from GTC Paris, Nicole chats with Andy Pernsteiner, Global Field CTO at VAST Data, for an insider's view of Europe's quickly evolving AI landscape. From the sidelines of Nvidia’s packed event—strategically co-located with Viva Tech—Andy shares candid insights on the shift from heavy-lifting AI infrastructure builds toward practical, profitable services built on those investments. Sovereign clouds take center stage, as Andy unpacks Europe's increasing emphasis on secure, traceable, and auditable data architectures designed around tight regulatory frameworks and national boundaries. He offers a close look at how European Neo-cloud providers are pushing toward innovative services like inference-as-a-service, making AI genuinely consumable and not just an expensive science project.
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