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December 29, 2025Podcast #145: Why Edge AI Matters with Oregon State University
If you follow StorageReview regularly, you might have seen the piece on the OSU Ocean Study in real time. We spoke with Chris Sullivan, Director of Research and Academic Computing at Oregon State University, to better understand how technology helps researchers study ocean life and how that impacts the global environment.
Brian met up with Chris at SC25 to discuss the topic further. It’s rare for us to write about how technology is used in real-world situations such as OSU’s plankton research, so this feature and the podcast gave us that opportunity.
As Brian and Chris discuss the research being conducted at OSU, there’s genuine excitement about how technology is advancing to support ongoing oceanic studies. Chris has been with Oregon State University for over 24 years and has served as director of biocomputing for the past three years.
This live podcast showcases Chris’s excitement for science and technology. He called SC25 “candy land.”
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This is a lively and honest discussion (with some humor) that provides genuine insights into how research uses technology to deliver real-time results.
It’s a short podcast worth listening to, but if you’re pressed for time, we’ve provided a transcript that lets you jump to the most relevant sections.
0:00 – 5:00
Turning a Research Vessel into a Floating Supercomputer
Chris Sullivan from Oregon State University explains how new tech is fundamentally changing how ocean science is done, especially massive plankton studies run from ships that are basically small, floating data centers.
Technology isn’t just “cool gear”; it directly changes what science is possible and the extent of bias in results.
Scientific bias: limited or skewed samples leading to inaccurate conclusions; the cure is more data points across more locations.
Goal: Collect huge, diverse datasets so statistical analyses aren’t distorted by sampling just one spot or one set of conditions.
Traditional HPC setups forced a trade-off: fast local NVMe/SSDs with limited capacity vs. large but slower tier-one storage systems.
For large experiments, Chris had to depend on tier-one storage across the cluster because on-node (tier-zero) storage was too small to hold massive datasets.
Big, shared-storage “super pods” became necessary to keep GPUs fed, but they’re expensive and not realistic to deploy in the field or at sea.
5:00 – 10:00
Ocean Robots, Plankton, and a Million-Dollar Cruise
This section dives into what actually happens on the ship—how they tow instruments, capture staggering amounts of image data, and why semi–real-time processing is critical when ship time costs about $1M per 10‑day cruise.
The new NSF-backed research vessel is built like a “mini Star Trek Enterprise,” packed with tech and about 200 miles of cabling on a 200‑foot boat.
Previously, Chris had to roll his own compute hardware onto the ship, string fiber down hallways, and operate without a proper on-board data center.
The plankton system: a towed device moves up and down through the water at 5 knots, pulling ~162 liters of water per second and generating ~10 GB of image data per minute.
A single 10‑day plankton experiment: ~100 TB of raw data; after processing, segmentation, and classification, that expands to ~300–400 TB of usable data.
They must process this in semi–real time; otherwise, a $1M, 10‑day cruise could return with bad or useless data and no opportunity to redirect the ship.
Plankton are mostly at the mercy of currents (not strong swimmers), yet they’re crucial: ~50% of Earth’s oxygen, ~25% of the carbon sink, ~17% of global food per capita, and the base of the marine food web.
10:00 – 15:00
Edge AI at Sea and in the Forest
The focus shifts from the ship to the broader “edge” problem: how to push compute closer to where data is created—on boats, in ranger stations, and across forests filled with autonomous sensors.
New high‑capacity SSD-based systems let them build a true tier zero on a single device: store hundreds of TB of data, process in semi‑real time, and never move it again.
Earlier tests with smaller NVMe drives required constant offloading to make room, which burned precious IO bandwidth and slowed the science.
There is essentially no dedicated IT staff on the ship; one person runs the ship systems and stays away from experiments, so grad students and postdocs must manage hardware and pipelines themselves.
This drives a design requirement: simple, robust pipelines and self-contained devices—no fragile web of arrays, mounts, and volumes.
At the forest edge (e.g., the Owl project), they run ~5,000 autonomous recording units and can identify ~130 bird species by sound alone.
The pain point: humans still have to go collect SD cards; by the time the data returns, much of the temporal value is gone. Edge processing aims to extract insights immediately at the point of capture.
15:00 – 20:00
Matching the Right GPUs and Storage to the Job
Here, they unpack GPU strategy, Omniverse-based training, and why large-capacity flash is as important as raw compute for AI and scientific visualization.
Chris stresses using the proper GPU for the job: large, expensive accelerators (e.g., H100/Hopper, “grasshoppers”) should focus on training, while more cost-effective cards like RTX 6000s handle inference at scale.
Historically, there was confusion: too many overlapping GPU SKUs, unclear “swim lanes,” and a lot of money wasted on overkill hardware for simple inference.
RTX 6000s give them a dual role: high-performance inference plus graphics and rendering capabilities that data-center-only GPUs lack.
They’re building an Omniverse digital twin of the ship to:
Train students and postdocs virtually while the ship is at sea.
Plan physical layouts and equipment loading in tight, moving spaces.
Avoid wasting expensive ship time on basic training and trial‑and‑error setups.
On a moving ship, anything with moving parts is a risk; solid-state storage is preferred for reliability and resilience.
High-capacity SSDs in a small power envelope free up power budget for more GPUs, reduce the need for separate arrays, and shrink the total hardware footprint.
20:00 – 25:00
From Filing Cabinets to Fiber-Connected Oceans
Zooming out, how cheap storage unlocked modern AI, how ocean observatories stream data ashore, and why network and storage together are enabling new kinds of science.
Chris argues AI’s real unlock was not new math—most models date back to the 1950s–70s—but the cheap hard drive of the 1990s, which moved data from filing cabinets onto searchable, computable media.
In the “filing cabinet era,” they had tons of data but were “information poor”—no search, no redundancy, and no scalable processing.
There’s always been an arms race between processing speed and data availability; large-capacity SSDs now tip the balance again by feeding modern GPUs with far more data.
Oregon State is the cyberinfrastructure lead for the Ocean Observatories Initiative (OOI), a billion‑dollar NSF/WHOI project that uses gliders, buoys, and seafloor mounts to stream ocean data.
They hold ~9 PB of OOI data, with a fiber backbone upgraded from 200 Gbps to 400 Gbps via Link Oregon, enabling real-time data transfer from the ocean to the data center.
Even with Starlink on the ship and on land projects (like the owl work), satellite bandwidth and cost limit what can be sent, so they’re testing LoRaWAN and other mesh networks to push data back to a single, Starlink‑connected ranger station.
25:00 – 27:52
Lessons for Enterprise: Your Data Center Is the Edge Now
The conversation closes by translating these research lessons to enterprises and highlighting the hardware Chris would “steal” from the show floor.
Core message to enterprises: if you’re not processing at the edge, you’re burning time and labor hauling raw data back and forth. And missing real-time opportunities.
Edge AI and agents should preprocess, filter, and analyze locally to redirect human effort from logistics to higher‑value tasks and decision-making.
Real‑world analogs:
Retail uses edge analytics in stores to track traffic, make recommendations, and deliver dynamic experiences, much like forestry uses edge inference to protect endangered species while enabling logging.
Fisheries want “smart traps” and buoys so they only send out boats when there are actually crabs in the pots.
For Chris, the ideal pattern is on-premises data centers that become edge facilities integrated with the cloud for large training runs and longer‑term workloads.
Favorite hardware from the show: Dell’s PowerEdge 7745; he wants more of them for VR labs and field deployments because they strike the right balance of power and footprint, flexibility, and capabilities (rendering + inference + edge work).
He sees systems like the 7745 as the template: flexible enough to live in a data center or in harsh field conditions, supporting both traditional HPC and advanced AI/visualization at the edge.
The post Podcast #145: Why Edge AI Matters with Oregon State University appeared first on StorageReview.com.
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28min
November 24, 2025Podcast #144: 300 GB/s in One Dell PowerEdge R7725xd
At the SC25 event in St. Louis, Brian caught up with his long-time friend Mark Klarzynski, founder and acting CTO at PEAK:AIO. If you have been following StorageReview, then you may be familiar with our coverage of PEAK:AIO, from regular news coverage to medical AI to animal conservation.
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As Mark explains, he has “spent a lifetime in storage, and he has known Brian for a very long, long time.” It shows with a very candid, easy conversation about hardware and software, and the evolution of both.
In this really short Podcast, Brian and Mark talk about software development, Dell’s PowerEdge, and GPU servers. Mark is excited about advances in hardware design, especially Dell’s PowerEdge R7725xd, powered by AMD EPYC CPUs.
This short podcast covers a lot of territory, and it is worth listening to in one sitting. However, if you can’t spare 20 minutes, we’ve broken it down into five-minute sections so you can hop around.
0:00 – 5:00
PEAK:AIO software unlocks the Dell 7725XD’s extreme performance.
Modern hardware (CPUs, NVMe, networks) is finally good enough that software can “get out of the way.”
PEAK:AIO removed decades of legacy storage features to get closer to bare metal.
Their surprise: the Dell box delivered far higher performance than expected; they thought the tests were wrong.
Philosophy: Making things simple and stable is more complicated than just making them fast.
5:00 – 10:00
PEAK:AIO chose to remain independent rather than become a feature within a prominent vendor’s product line.
They work directly with research teams (e.g., brain cancer models), which is more rewarding than just speeding up databases.
Early focus: compressing what previously required 4–10 storage nodes into a single powerful server.
Traditional parallel file systems were built for 10GbE + HDD; NVMe and massive PCIe bandwidth are now required.
With Los Alamos, they’re building an open, modern NFS fabric—no proprietary lock‑in.
10:00 – 15:00
Hardware “wish list”: more PCIe lanes and PCIe Gen 6 to unlock even more bandwidth.
Vision: GPU servers that are a model box, inference box, and storage node all in one, yet part of a larger namespace.
Start small on a GPU server; when storage runs out, add a 7725XD and let PEAK:AIO automatically expand and rebalance.
Customers want to scale storage only when AI projects prove value, not buy a giant array up front.
Economics matter: you can’t justify a $1M storage array behind a $300k GPU server for hospitals and similar budgets.
15:00 – 20:00
Strategy: Use commodity servers plus PEAK:AIO software to replace expensive, specialized storage arrays.
OEMs like Dell handle fundamental hardware issues; PEAK:AIO focuses on software, stability, and performance.
Built-in monitoring and predictive intelligence; years in the field with no downtime from their platform.
Joint project results: ~70M IOPS in a single node and hundreds of GB/s throughput—record numbers for them.
Next step: put models inside the storage node (with spare CPU/GPU capacity) so inference happens in-box, not over the network.
The post Podcast #144: 300 GB/s in One Dell PowerEdge R7725xd appeared first on StorageReview.com.
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20min
November 16, 2025Podcast #143: KIOXIA 245TB SSDs Are Here!
KIOXIA’s Maulik Sompura joins Brian for an in-depth, informative discussion about all things flash and other industry events. The topic is timely and relevant, given the massive expansion of AI and modern workloads.
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Maulik Sompura is the Senior Staff Director—Product Planning and Management at KIOXIA, with over 13 years of experience in NAND, memory, and SSDs, including the last six years at KIOXIA. Additionally, his career includes stints with Intel and Toshiba.
The conversation extends beyond storage and memory, transitioning to liquid cooling, hyperscalers, and form factors. They also discuss open-source software, new fabs, concerns about scaling, and fears of another “Covid-boom-and-bust” event.
It is an informative conversation that will prompt you to think about something in the long term. We’ve broken the podcast into five-minute segments, so you can jump to the section that’s most relevant to you and your environment. However, it is well worth the time to watch this in its entirety.
Brian and Maulik kick off the discussion with how radically SSDs and flash storage have evolved, especially under the pressure of AI and hyperscale data centers.
0:00–5:00: Hyperscalers Quietly Running the Show
SSD portfolios have exploded in complexity: many form factors, endurance levels, and performance tiers.
The AI boom has moved storage right behind GPUs/HBM as a first-class priority.
Hyperscalers (Meta, Google, Amazon, etc.) dictate detailed drive requirements, and those custom designs ultimately shape what enterprises get.
OCP specs help unify and simplify the chaos by giving everyone a common target on top of NVMe.
The industry is actively trying to shrink the number of SKUs and converge on modern form factors like E3, even as legacy form factors linger.
5:00–10:00: E3, E2, and Petabyte-Class Drives
E3 is becoming the preferred enterprise form factor for Gen6+ due to its better power and thermal characteristics.
2.5″ drives will stick around for years due to long product lifecycles, but new designs are moving to EDSFF.
E2 is a larger, capacity-first SSD form factor, seen as a true hard drive replacement at extreme capacities.
With advanced NAND stacking, a single E2 drive could eventually reach 1 PB.
Hyperscalers mitigate the risks of giant drives using sharding, erasure coding, and predictive analytics; rebuild times for huge SSDs are still much better than for large HDDs.
10:00–15:00: Liquid Cooling Comes for SSDs Too
AI training systems are driving widespread adoption of liquid cooling, and that now extends down to storage.
SSDs in E3 form factors are being redesigned for direct contact with cold plates (surface flatness, materials, and connector details).
Liquid-cooled Gen5 SSDs will pave the way for Gen6, where power and heat go even higher.
Enterprises are cautious about bringing water into the data center, but rising power costs and GPU density will make liquid cooling harder to avoid.
15:00–20:00: AI Storage: Capacity, Performance, and Fabric Stress
AI pushes storage along two axes: massive capacity and very high performance.
KIOXIA’s 245 TB-class drive (with 32‑high QLC stacks) targets data lakes and large repositories, not just generic workloads.
Inside a modern box, storage can push 250–280 GB/s; the bottleneck increasingly becomes the network fabric, not the drives.
Hyperscalers are racing to 400/800 GbE and smarter NICs/DPUs, while mid-market customers struggle to keep up with that level of fabric investment.
Traditional databases and Fibre Channel remain, but vector databases and AI-centric data stores are joining the mix.
20:00–25:00: Smarter Software and a True Flash Super‑Cycle
KIOXIA isn’t just shipping drives, it’s investing in software (e.g., ISAAC) to reduce HBM/DRAM needs while preserving AI performance.
The strategy is holistic: improve cost and efficiency at the system level, not just through faster NAND.
New fabs like Kitakami (K2) are ramping up, but vendors are cautious after Covid-era boom-and-bust cycles that cost the industry ~$30B.
Scaling NAND (more layers, finer lithography) is getting more expensive and faster-paced, so that supply will grow, but in a controlled way.
Many see this as a multi-year “super‑cycle” driven by AI and data growth, not just a normal 1–2-year flash-upturn.
25:00–End: AI Everywhere, Flash vs. HDD, and Learning from HPC
Data keeps growing and is rarely deleted; AI is moving to wherever that data lives, including the edge, backups, and secondary tiers.
High-capacity SSDs can deliver ~100 PB per rack, versus multiple racks of HDDs for the same capacity, with far better performance and efficiency.
HDD performance doesn’t scale with capacity; SSDs deliver orders-of-magnitude better IOPS/GB and throughput, improving TCO and favoring flash at scale.
Shows like OCP and Supercomputing (SC) are where the future is visible first: wide racks, 800V busbars, exotic cooling, and power designs.
Hyperscaler and HPC innovations are steadily shaping what “normal” enterprise servers and data centers will look like, with resiliency and efficiency.
The post Podcast #143: KIOXIA 245TB SSDs Are Here! appeared first on StorageReview.com.
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40min
November 12, 2025Podcast #142: What’s Next For Storage in the AI Era?
Brian and crew recently landed in Montreal to tour the Hypertec lab facility and witness some interesting immersion-cooling trends with the leader in the field.
Following the lab tour and product demos, Brian sits down with Scott Shadley, Director of Leadership Narrative at Solidigm. Scott and Brian have a lively, candid discussion as they trace the evolution of cooling, flash storage, and breaking away from legacy hard-drive form factors.
However, it’s not all about immersion technology. Brian and Scott discuss signal integrity challenges in PCIe Gen5/6 and the trade-off between performance and reliability. Of course, there can’t be a podcast without an AI segment, so we cover topics such as AI-driven data growth, RAID resiliency, and large-drive “blast radius” management.
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Scott discusses Solidigm’s focus on developing products that customers want, rather than guessing what the industry needs. This only works when vendors work closely with customers to understand their pain points. Scott gets into the collaboration with NVIDIA. It is easy to hear the excitement in Scott’s voice. Of course, because it is a massive hardware environment, Brian has a hard time wiping the smile from his face.
This podcast covers everything from early Fibre Channel drives to flash and NVMe, and also explores edge computing applications, AI inference offload, and immersion-qualified SSD developments.
This pod is a must for anyone interested in the evolution of immersion cooling and flash storage, as well as why a strong partner ecosystem is crucial for customer success. If you have an extra 35 minutes, give this a listen or watch it from end to end; it’s worth the time. If you’d like to explore specific topics, we have broken it down into five-minute segments.
0:00 – 5:00 | Immersion Cooling and Industry Trends
Highlights:
Opening from Montreal with Solidigm and Hypertec.
Discussion of immersion cooling, liquid vs. air cooling, and touring Hypertec’s facility.
Mention of OCP and how data center engineers are “becoming plumbers” due to widespread liquid‑cooled infrastructure.
Solidigm’s collaboration with partners on direct‑to‑chip liquid cooling solutions is co‑designed with NVIDIA.
Philosophy of open innovation: Solidigm contributes IP to industry standards via SNIA to ensure interoperability.
Emphasis on customer‑driven innovation over chasing the next PCIe or interface generation.
5:00 – 10:00 | Evolution of Flash Storage and Form Factors
Highlights:
Reflecting on early enterprise flash (Intel X25, SAS/Fibre Channel SSDs).
Transition from legacy hard drives to SSDs and the “breaking the box” moment with NVMe.
Emergence of new form factors (EDSFF → E1.S, E1.L, E3).
Discussion about how standardization is evolving beyond traditional 2.5” and 3.5” drive designs.
Design challenges with immersion environments and high‑density flash platforms.
Importance of thermal stability: consistent environments improve endurance and reliability.
Cooling innovations directly tie to longer SSD lifespans and greater efficiency.
10:00 – 15:00 | PCIe Generations, Signal Integrity, and System Design
Highlights:
Talk about connector design challenges in next‑gen cold‑plate and PCIe Gen 5 and 6 environments.
Signal integrity issues grow from PCIe 3→5→6: Every generation increases susceptibility to noise.
Analogy: PCIe evolution from 3G to 5G: more nodes, shorter distance, tighter tolerances.
Solidigm’s view: optimize system architecture and density instead of chasing raw drive speed.
Many enterprise customers remain on Gen4 because it’s “fast enough” for distributed workloads.
Shift from pure performance toward holistic throughput, reliability, and efficiency.
15:00 – 20:00 | Reliability, Capacity, and Customer Trust
Highlights:
Performance is balanced with quality, longevity, and consistency.
SSDs reaching “reliability of a DIMM.” Many customers stop swapping drives; they fail “eloquently.”
Customer satisfaction and trust are top priorities; Solidigm scores highly in vendor ratings.
Rise of ultra‑dense drives like 122TB models (D5‑P5336 family) and discussion of adoption.
Larger capacities are no longer halo products.
Manufacturing challenge: Smaller drives are harder to produce as NAND density increases.
Drives sized for total cost of ownership (TCO) optimization; approach to “replace spinning disks.”
20:00 – 25:00 | Data Growth, AI, and Blast Radius Concerns
Highlights:
Industry shift from deleting data to keeping everything, fueled by AI training and inference needs.
“Data sovereignty” trend: Regional training data sets lead to more localized capacity requirements.
Growing demand for massive local storage to adapt global models like GPT.
Discussion of “blast radius” and the risk of losing a 100 TB drive.
Hardware RAID for NVMe is back; GPU‑accelerated parity rebuilds are gaining traction.
SSD rebuilds are fast. Downtime rather than permanent data loss is the genuine concern.
Many operators now design for failure‑in‑place architectures instead of manual swaps.
25:00 – 30:00 | Future Directions, AI Edge, and Innovation Opportunities
Highlights:
Reflection on industry-wide stock shortages driven by the AI boom and NAND supply lags.
Unique opportunity to rethink architectures and possible new form factors or integrated flash + networking.
Edge innovation: NVIDIA’s Spark and Jetson products blur the boundaries between data centers and desktops.
Exploration of NVMe offload for inference and retrieval-augmented generation (RAG).
Hope for a future where storage innovations take center stage at GTC alongside compute and DRAM.
Emphasis on cross‑industry collaboration. SNIA’s new “Storage.AI” working groups integrate storage, networking, and AI alignment efforts.
30:00 – 34:00 | Closing Thoughts and On‑Site Testing
Highlights:
Returning to Hypertec’s immersion servers for real‑world tests with Solidigm drives.
Discussion of thermal stability advantages: consistent temperatures equal no throttling.
Solidigm is working toward fully qualified SSDs for hydrocarbon‑based immersion liquids.
Closing conversation: Mutual excitement about hands‑on experimentation, performance consistency, and hardware innovation.
Wrap‑up and goodbyes from Montreal.
The post Podcast #142: What’s Next For Storage in the AI Era? appeared first on StorageReview.com.
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35min
October 26, 2025Podcast #141: Graid Delivers Enterprise RAID Functionality to AI Workloads
Gain insight into improving RAID performance for AI workloads with Graid SupremeRAID AE.
Storagereview has worked with Graid on numerous projects, including our most recent review, Storage at GPU Speed, and they continue to impress. In this podcast, Brian chats with his friend, Kelley Osburn, Senior Director of OEM and Channel Business Development at Graid Technology.
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Kelley is responsible for developing and executing the OEM and channel sales strategy, managing key partnerships and relationships, and expanding the company’s market presence and revenue. He leverages his expertise in flash memory, data storage, virtualization, cloud, DevOps, and containers/Kubernetes to deliver value-added solutions to our customers and partners.
Graid Technology delivers enterprise-grade RAID functionality for AI workloads with minimal infrastructure changes. Instead of a dedicated hardware RAID card or custom appliance, AE is offered as a software license and uses only a small share of an existing NVIDIA GPU. This enables organizations to achieve enterprise-grade storage performance and reliability without consuming additional PCIe slots, requiring infrastructure changes, or significantly impacting GPU performance for training and inference workloads.
Brian and Kelley dive into the functionality of the Graid solution, why it’s critical in today’s AI landscape, and what the future holds.
If you are interested in improving RAID performance for AI workloads without adding more GPUs to your existing racks, this podcast delivers.
If you don’t have time to listen to the entire podcast, we have broken it down into five-minute segments so you can easily hop around to get the information you need.
Listener’s Guide
Why Graid? Problem Solved! [00–05]
Traditional RAID/MD-RAID hit a wall with NVMe and PCIe Gen4/5/6—hard x16-lane bottlenecks.
Unsafe stopgaps: RAID 0 + snapshots/checkpoints increase risk and admin overhead.
CPU-bound math in MD-RAID diverts cycles from apps; GPUs excel at parallel parity/EC math.
Graid offloads to the GPU and uses peer‑to‑peer paths, so data bypasses the card (no x16 choke).
Anecdote: 16–44 drive servers magnify the physical lane mismatch of legacy HW RAID.
Outrunning “x16 limits” [05-10]
Affordable GPUs (A2000/A400) act as a traffic cop, not a data ferry—peer‑to‑peer NVMe routing.
Real builds: ~200 GB/s with ~24 drives; new paths push ~300 GB/s/server.
Design tradeoffs: ~24 x4 drives for peak perf; 40+ drives at x2 favors capacity/IOPS.
Small form factor: Low‑cost edition supports up to ~12 drives—ideal for AI desktops.
Banter: “How can it be faster than x16?”—because the data doesn’t traverse the GPU.
AI Edition: Use existing compute GPUs [12–15]
Run Graid on H100/Blackwell, you already own; no extra slot for a RAID GPU.
Tiny footprint: ~6/144 SMs on H100; “drop out” when idle to free resources.
GPU Direct Storage moves data from NVMe to GPU memory directly (lower latency/hops).
Keep mid‑riser slots for 400/800G NICs and other accelerators.
Anecdote: “Feed the Beast.” Keep GPUs from starving on IO.
Lab impact and edge patterns [15–20]
Modest tokens/sec impact during inference; storage and AI seldom peak at once.
Edge deployments: Single 2U with 2 GPUs for capture, light train, and inference at scale.
Preserve IO slots by sharing existing GPUs with Graid.
Examples: YOLO-style wildlife, retail, and other ready-to-use inference models.
Tip: Size storage to avoid starvation under bursty pipelines.
Integrity, namespaces, and scale [20–25]
RAID is more relevant at the edge: Resilience keeps GPUs productive during faults.
Transient errors: Detect bad reads, rebuild from on-the-fly parity, and relocate data.
Big namespaces: 61/122/256 TB-class drives make large protected pools valuable.
Scale via NVMe‑oF JBOFs (RoCE/RDMA); 24–96+ raw devices still require software RAID.
Roadmap: Higher drive counts + erasure coding for flexible protection domains.
Beyond AI: Databases, streaming, analytics [26–30]
Databases/HFT: Redis, Aerospike, and Oracle benefit from fast, consistent writes.
Splunk/log ingest: Sustained throughput prevents drops and backpressure.
Media: Multi-8K stream servers can reduce the total server count for a workload.
Density/power constraints favor higher perf per RU in colocations.
Rebuilds: Parity flows through GPU; slight latency increase, rarely app‑visible.
Roadmap, PCIe Gen6, how to engage [30-35]
Software-first: moving Gen4 to Gen5 boosts perf since Graid isn’t in the data path.
Gen6 flash (~28 GB/s x4) magnifies the need to remove chokepoints.
Peer-to-peer + GPU-offloaded math unlocks new PCIe generations without HW churn.
How to buy: Dell catalog, Supermicro OEM, channels (CDW), and Amazon.
Meet Graid: An NVIDIA Inception partner with booths at GTC Washington & San Jose.
The post Podcast #141: Graid Delivers Enterprise RAID Functionality to AI Workloads appeared first on StorageReview.com.
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36min
September 27, 2025Podcast #140: Inside HPE SimpliVity with Morpheus VM Essentials
HPE integrates Simplivity Hyperconverged HCI plus Morpheus VM Essentials stack. Get the details in this podcast.
Brian and the team traveled to Spring, TX, to get an up-close look at what HPE is working on. After the tour, Brian sat down with HPE’s Vinay Jonnakuti, Director, Product Management for HCI and Ecosystems, to unpack the newly integrated SimpliVity plus Morpheus VM Essentials stack. They discuss why integrated HCI is important now, the benefits of two-node high availability at the edge, cost savings with per-socket licensing, and how Morpheus combines hybrid cloud and automation. It also includes real-world retail and AI at the edge examples.
Brian and Vinay’s friendship goes way back, making this podcast interactive and fun. They do a bit of reminiscing, talk about the Silicon Valley TV show from 2016, and the fact that Vinay still has the Pied Piper t-shirt.
Then, it’s time to get down to business. HPE is strengthening its vertically integrated HCI stack by combining SimpliVity’s efficient data platform with Morpheus VM Essentials as the control plane. The result is a fully supported, single-vendor solution covering servers, networking, hypervisor, and management. Customers benefit from two-node HA, integrated backup and deduplication, automated deployments and upgrades, and per-socket licensing that lowers TCO, especially for SLED, retail, and distributed enterprises. Morpheus adds hybrid cloud orchestration to the platform, while ProLiant hardware accelerates the adoption of modern NVMe, CPUs, and NICs. Expect regular release schedules and more ISV-backed solutions for the retail, healthcare, and manufacturing sectors.
There is a lot to learn from this podcast. And it’s only 30 minutes! But if you don’t have the time to watch it in one sitting, we have broken it down into five-minute segments, highlighting the topics covered in each segment.
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00:00–05:00
Introduction: Brian and Vinay set the scene at HPE in Spring/Houston. Brian introduces Vinay and asks him to describe his role leading product management for HPE SimpliVity and recall the pre-acquisition days and pop-culture tie-ins (Silicon Valley/Pied Piper bezel).
The situation: Transition from nostalgia to today’s focus on HPE’s vertically integrated stack, SimpliVity HCI, and HPE Morpheus VM Essentials as part of HPE’s end-to-end story.
05:00–10:00
Market dynamics and alternatives: Discussing VMware cost pressures, especially for SLED and smaller organizations, and exploring the landscape of alternatives, including support gaps in Proxmox, Nutanix costs, Azure Stack options, and other niche players.
HPE’s integrated differentiation: HPE’s unique “soup to nuts” approach, with servers, networking, hypervisor, and control plane, delivering a fully integrated, single-vendor solution with SimpliVity + Morpheus VM Essentials.
Morpheus scope: Morpheus Enterprise’s broad automation/customization capabilities and how VM Essentials provides a focused subset tailored to HCI management.
10:00–15:00
Operational simplicity and cost: Automated deployment/upgrades through the VM Essentials Manager; positive beta feedback; per-socket licensing helps TCO (no per-core tax), aligning well with budget-constrained sectors.
Feature depth retained: Built-in migration tooling, integrated backups, data efficiency (dedupe/compression), and day-one parity for core SimpliVity capabilities with a roadmap to expand features.
Two-node advantage: Unique two-node HA architecture (with lightweight witness) maintaining two copies of data, enabling predictable performance, resilience, and lower cost versus 3–4 node minimums elsewhere.
15:00–20:00
Portfolio fit and scale: Clear positioning for SimpliVity at the edge, distributed enterprise, and smaller clusters, with options to scale up through HPE’s broader storage portfolio (Electra MP/B10K) and similar management experiences.
Cadence and alignment: Tight release cadence: Morpheus VM Essentials shipping frequently (monthly to ~6-weekly) and SimpliVity targeting multiple releases per year, adding features, hardware support, and deep GreenLake integrations.
Unified platform vision: GreenLake + OpsRamp + Morpheus + ProLiant compute form the backbone for hybrid cloud management, observability, and orchestration with SimpliVity as a key edge component.
20:00–25:00
Hybrid cloud reality: The “world is hybrid” message—Morpheus can manage multiple hypervisors and public clouds alongside SimpliVity for a unified control plane and visibility.
Hardware matters (again): Emphasis on modern ProLiant-based platforms enabling faster adoption of NVMe, larger drives, faster CPUs/DRAM/NICs, and how moving off custom accelerators increases agility.
Edge AI use cases: Retail-edge scenarios (video analytics, loss prevention, restock, shopper intent, touchless/self-checkout) benefit from local inference; SimpliVity nodes with NVIDIA support can host AI/analytics VMs for real-time insights.
25:00–30:00
Vertical solutions and ISV ecosystem: HPE partnering with ISVs in retail, healthcare, and manufacturing to deliver validated joint solutions on SimpliVity with Morpheus control, combining resilience and data protection with domain apps.
Customer engagement: Houston Customer Briefing Center demos (rack of HPE gear + SimpliVity node), upcoming collateral and videos, and guidance to engage HPE partners/field for evaluations.
Wrap-up: Closing remarks and availability timing for the integrated SimpliVity + VM Essentials solution. Look for links to a deeper technical dive video and white paper accompanying the podcast.
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0min
July 24, 2025Podcast#139: Immersion-Cooling, The Valvoline Way
Following a tour of the facillities, Valvoline’s VP of R&D, sat down with Brian to go deep on immersion-cooling.
Brian and the StorageReview team traveled to Lexington, Kentucky, to meet with Valvoline’s immersion-cooling pit crew to examine the practical, technical, and environmental aspects of immersion and direct-to-chip cooling. They highlighted the significance of material compatibility, the development of industry standards, and Valvoline’s ongoing R&D efforts to create fluids that are more sustainable and effective for the data center industry.
The hosts graciously gave a tour of the Valvoline HQ, which included some unique historical insights, followed by a look at race cars on display. Brian, a true motor racing fanatic, was in heaven.
Brian was pulled from the race cars and sat down for a conversation with Dr. Z. George Zhang, Vice President of R&D at Valvoline Global, about current and future trends in immersion cooling.
Dr. Zhang has been with Valvoline for over 26 years and is highly respected within the liquid-cooling community. He recently joined the Advisory Board for the North America Lubricant Expo & the Bearing Show.
He is a certified lubrication specialist (CLS) and oil monitoring analyst (OMA I). Dr. Zhang is an inventor or co-inventor on 8 U.S. patents and has authored 49 peer-reviewed publications.
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With all the buzz around liquid cooling, this podcast is a must-listen for anyone interested in immersion cooling technology. If you’re strapped for time, we’ve broken it down into five-minute segments to help you navigate this important topic.
0:00 – 5:00
Introductions & Backgrounds: Brian introduces Dr. George Zhang from Valvoline, highlighting Valvoline’s long history in automotive lubricants and innovation (like high-mileage oils and MaxLife).
Motor Oil as Engineering: Discussing how people often overlook the complexity of motor oil. It’s not only about lubrication but also cleaning, sealing, and heat transfer.
Heat Transfer Parallels: Dr. Zhang explains how expertise in automotive heat management translates to data center cooling, especially as data centers face massive heat loads from high-power GPUs.
Data Center Energy Use: Fun fact: ChatGPT’s 2024 energy use is comparable to the entire country of Ireland, highlighting the scale of heat management required.
5:00 – 10:00
Mission-Critical Differences: Data centers operate 24/7, unlike cars, so uptime and stability are crucial; however, the environment is less chemically harsh than that of automotive engines.
Material Compatibility: Data center hardware utilizes various metals, plastics, and labels, differing from those used in cars, making fluid compatibility a key concern. Labels and plastics can break down in immersion cooling environments.
Cleanliness & Purity: In direct-to-chip cooling, fluid purity is critical because tiny channels in cold plates can clog easily, impacting cooling performance.
Cooling Segments: Air cooling is still dominant, but liquid cooling (direct-to-chip and immersion) is growing. Valvoline is active in both, with a focus on their PG 25 fluid for direct-to-chip.
10:00 – 15:00
Technical Differentiation: Not all PG 25 fluids are the same. Valvoline’s patented formula utilizes low-solids and liquid-based additives to prevent deposits and salt bridges.
Condition Monitoring: Valvoline leverages its automotive expertise to help customers monitor the health of their fluids and system conditions, supporting global supply chains.
Ecosystem Complexity: Liquid cooling involves numerous partners, including server vendors, hose and cold plate manufacturers, quick-connect suppliers, and more. Collaboration and communication are key to avoiding finger-pointing when issues arise.
Water Quality & Sensors: Data centers often lack expertise in water quality, so Valvoline is collaborating with sensor manufacturers to enable real-time monitoring of fluid properties, such as turbidity and conductivity.
15:00 – 20:00
Sensor Integration: The future will include more sensors in cooling loops for early detection of issues, potentially even at the connector or cold plate level.
Market Adoption: Most US data centers are adopting cold plate/direct-to-chip cooling because it can be retrofitted, while immersion cooling requires new infrastructure.
Immersion Cooling Advantages: Immersion can cool 100% of components and is vastly more efficient at heat transfer than air, but requires careful fluid movement to avoid hot spots.
Thermal Conductivity: Immersion fluids (hydrocarbons) are chosen for their low electrical conductivity (dielectric properties), allowing for the safe submersion of electronics.
20:00–25:00
Immersion Cooling in Practice: Discussing different immersion cooling setups, such as tank immersion (servers dunked vertically) and their impact on power efficiency (PUE as low as 1.03–1.04, significantly better than air cooling).
Component Reliability: Immersion cooling can significantly reduce failure rates for components such as RAM, storage, CPUs, and GPUs. However, it introduces new operational challenges, such as handling servers with cranes and dealing with oil drips.
Fluid Chemistry: Valvoline uses pure hydrocarbon fluids for immersion—no fluorine or chlorine, just carbon and hydrogen. This ensures chemical inertness, similar to air, and avoids reactivity with server components.
Material Compatibility: Early lessons included issues with cables, labels, and plastics not designed for use with oil, such as color leaching, labels falling off, and plastics becoming brittle. Compatibility testing is now a significant focus.
25:00–30:00
Industry Standards & Collaboration: Valvoline collaborates with OCP (Open Compute Project) and ASHRAE to develop and adhere to material compatibility lists for both immersion and direct-to-chip fluids. These lists help customers choose the right materials for liquid cooling.
Networking & Signal Integrity: Concerns exist about high-speed networking and light transmission through oil; however, most hardware manufacturers now relocate switches out of the fluid. Valvoline hasn’t seen major signal issues but can adjust fluid dielectric properties if needed.
Industry Perception: Valvoline is often considered “the car guys” at IT events, but they emphasize their expertise in heat transfer and chemistry, which is directly relevant to data center cooling.
OCP Working Groups: OCP has separate groups for immersion and direct-to-chip cooling, showing the industry’s growing seriousness about liquid cooling.
30:00–35:00
Alternative Immersion Designs: Discussion of “precision immersion,” like Isotope’s approach, where fluid is sprayed directly onto hot components in a rack, using less fluid and allowing for easier retrofits. These designs can be more energy-efficient and fit standard racks.
Component Coverage: Precision immersion ensures that all components, including those that do not generate significant heat, such as capacitors and transistors, are effectively cooled, potentially improving overall reliability.
Multiphase Cooling: Multiphase (two-phase) cooling utilizes the heat of vaporization for enhanced efficiency; however, the fluids used are often PFAS (“forever chemicals”), which are environmentally problematic. Valvoline avoids these for sustainability reasons.
System Complexity: Multiphase systems require sealed environments to prevent gas escape, adding complexity and risk.
35:00–37:00 (End)
Fluid R&D and Future Directions: For immersion fluids, Valvoline is researching ways to reduce its carbon footprint, including bio-derived hydrocarbons, although these present challenges due to the presence of double bonds and higher costs.
PG 25 (Direct-to-Chip) Sustainability: PG 25 is currently derived from petroleum, but plant-based options are available, although they are pricier. Valvoline is exploring ways to make these more cost-effective and sustainable.
Chemistry Matters: Chemistry knowledge is increasingly essential for IT professionals working with advanced cooling systems, compatibility, fluid properties, and environmental impact, all of which hinge on a solid understanding of chemistry.
Lab Testing: Valvoline does extensive in-house testing for compatibility, performance, and system efficiency, both for immersion and closed-loop cooling systems.
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40min
June 06, 2025Podcast #138: Solidigm Liquid-Cooled SSDs
Providing a look at the latest in liquid-cooled SSDs from Solidigm.
Brian invited Cody Noland, Solidigm’s Sr Thermal Mechanical Engineer, to the show to discuss the latest in liquid-cooled SSDs. The Solidigm liquid-cooled D7-PS1010 E1.S, the industry’s first cold-plate-cooled SSD. The drive was introduced during GTC 2025 and garnered massive attention during the event and after. Solidigm’s proprietary E1.S SSD case design efficiently channels heat from the non-contact side through the case to the side that interfaces with the cold plate. This promotes efficient cooling on all sides of the drive, improving thermal performance and reliability during demanding workloads.
Cody has been working as a thermal mechanical design engineer for most of his career, designing solutions for companies such as Intel and CoolIT. He performs complex thermal analysis on PCBs with multiple power sources influencing board placement and optimizing conduction paths. Cody is skilled in CFD, CAD, Solidworks, DFMEA, DFM, DFA, CNC, CAM, Thermal data acquisition, thermal analysis, data analysis, server thermal analysis, mechanical tolerance analysis, and HVM design. It is apparent where his interest lies.
Cody’s hobbies included using his CNC Router, woodworking, and welding.
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This is a technical discussion on cooling and thermal design. This is a shorter-than-normal podcast, so if you can spare 30 minutes, it’s worth your time. If not, we have broken it down into five-minute segments, so you can find the topics that interest you.
0–7 min:
Why Liquid Cool SSDs? (And Who Let the Engineers Out?)
Introducing Cody from Solidigm
Highlighting Solidigm’s GTC demo: liquid-cooled SSDs
Pairing cold plates and storage, usually reserved for CPUs and GPUs
What inspired Solidigm to take the plunge
Data centers are evolving from air to hybrid to full liquid cooling
Pushing the market forward by showing that storage can (and maybe should) be part of the liquid cooling revolution
The goal: keep SSDs cool without sacrificing serviceability or requiring fancy tools
Reminiscing about dunking Solidigm drives in Castrol fluid for a previous video, highlighting the humor (and mess) of hot-swapping SSDs in oil
Can we cool the whole system, including storage
7–11 min:
The One-Sided Cold Plate
Solidigm’s demo utilized E1.s drives, which are popular with hyperscalers
Good fit for liquid cooling experiments
The cold plate only contacts one side of the SSD, saving space and adding more drives
Are there changes to the PCB
Some internal tweaks to optimize heat transfer, but the core drive remains familiar
11–17 min:
Performance, Power, and the Next Gen of SSDs
Does liquid cooling just match air cooling, or does it actually boost performance
Liquid cooling is a game-changer, pushing SSD power and performance beyond what’s possible with air cooling
Power management
What does the future hold for Gen6 SSDs
Power is going up, and connector limitations are a factor
Pushing the boundaries for specific applications
Will Solidigm patent this, or will it be open-sourced
17–22 min:
From Labs to Parking Lots, The Wild World of Cooling
The explosion of liquid cooling vendors and the rise of immersion cooling
Wild deployments like DUG’s 440+ immersion tanks
It’s an exciting time to be a thermal engineer
A significant surge in cooling innovation
Solidigm is ensuring its drives can handle air, immersion, and direct liquid cooling
Immersion cooling can lower failure rates, but will liquid cooling make SSDs even more reliable
22–27 min:
GTC Demo Reactions: “What Is Going On Here?”
What did people think of the GTC demo
The most common reaction was, “What is going on?”
The demo sparked a lot of industry chatter and got people thinking about 100% liquid-cooled systems and new ways to design dense storage.
Wrap-up:
The Future of Liquid-Cooled Storage
Wrap up, noting that liquid cooling is here, especially for high-density AI servers
Hats off to Solidigm for showing off new tech before it’s even on the market
Thanks to Cody for escaping the thermal lab to share his insights
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30min
April 25, 2025Podcast #137: AI Is Forcing Data Centers to Go Liquid
Explore how 2MW liquid cooling is transforming AI data centers with new levels of efficiency, scalability, and thermal management.
If you attended Data Center World 2025 in Washington, DC, it would have been challenging to walk far before finding another liquid cooling solution. Each is unique with a special sauce. We have been following the innovations to get more from the compute side, and most solutions point to liquid cooling. One of the companies we have worked with is CoolIT. They just released the CHx2000 at DCW, and we were there to talk to Luca Cutrone, Director of Business Development at CoolIT.
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Luca’s LinkedIn profile describes him as a “fun-loving technophile” with over a decade of leadership experience. Following a seven-month break, Luca joined CoolIT in January 2024, and it appears to be a great fit. Luca’s focus throughout his career has been on customer success, and he carries that mission forward in his work at CoolIT.
Brian caught up with Luka Cabrone in the CoolIT booth at Data Center World to discuss the latest innovations in liquid cooling. The conversation focused on CoolIT’s new product launches, the evolving data center cooling landscape, and the practical challenges of implementing advanced cooling solutions in today’s high-density environments. It was recorded live on the show floor.
More information about CoolIT can be found in Brian’s review of their direct liquid-cooled architecture.
If you are interested in liquid cooling technology, this podcast is for you. A lot of ground is covered in this short podcast. However, if you don’t have time to watch it in one sitting, we’ve broken down the conversation highlights into five-minute segments so that you can skip around.
00:00–05:00: Introduction
CoolIT’s new 2000 liquid CDU is capable of rejecting up to 2 megawatts of heat.
The growing need for liquid cooling in high-density chips and AI is discussed.
Educating the market on transitioning to liquid cooling.
05:00–10:00: Innovations
The real-world benefits of liquid cooling include power savings and reduced fan usage.
A University of Chicago case study shows a 37% power savings in a lab environment.
Quieter, cooler labs are a benefit of liquid-cooled environments.
Optimizing power delivery is essential as rack densities increase.
10:00–15:00: Immersion cooling
The chemistry behind CoolIT’s cooling systems— using a propylene glycol-water mix.
Emphasis on filtration and system cleanliness for cold plate microchannels.
Single-phase vs. two-phase cooling, with CoolIT focused on single-phase.
Immersion cooling and the engineering behind CoolIT’s 4000-watt cold plate.
15:00–20:00: How to acquire CoolIT solutions
The buying process for CoolIT’s solutions and integration into operations is explained.
CoolIT works with OEMs for factory integration, and facilities teams manage the infrastructure.
OCP is pushing for standardization, but challenges remain around warranty and compatibility.
20:00–End: Challenges
The future challenges of managing increasing heat loads are discussed.
The demand for innovative, smart operational tools, such as leak detection and AI-driven monitoring.
Industry collaboration on standards is essential.
CoolIT is committed to innovation and supporting the data center ecosystem.
As we were walking through the exhibit floor, it was clear that liquid cooling is no longer a niche technology, but a critical enabler for the next generation of data centers.
CoolIT
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0min
January 17, 2025Podcast #135: HPE Alletra Storage MP Sanjay Jagad
Brian talks with HPE’s VP of Product Management for Cloud Data Infrastructure while on-site at the HPE facility in Spring, TX. Listen as they discuss the Alletra Storage MP system.
Brian recently visited HPE’s headquarters in beautiful downtown Spring, TX. He invited Sanjay Jagad, a good friend of Storagereview.com, to join him for a detailed discussion about storage and HPE Alletra Storage MP. They discussed the factors driving the storage industry today and how MP has impacted storage system construction.
We have a full write-up and long-form video on YouTube to get up to speed on the platform.
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This 40-minute podcast covers a wide range of topics, and it is well worth your time if you can spare it. Otherwise, we have time-stamped the recording so you can skip around as needed.
0:00-5:00 Introduction
HPE Campus Visit & Introduction to Alletra MP Storage
Brian visits HPE’s headquarters in Texas and introduces the Alletra Storage MP.
Discuss HPE’s universal storage platform, which separates hardware from operating environments. Provides flexibility and high availability.
Overview of business benefits and future of storage from HPE’s perspective.
Evolution of Storage
Sanjay explains the traditional two-controller storage system and how the market is evolving to meet modern demands, such as increased data and distributed applications.
5:00 -11:00 Introduction to Alletra Storage MP
HPE’s Reimagined Storage Approach
Introduction to Alletra MP, HPE’s reimagined storage solution.
Focus on simplifying hardware designs with modular building blocks and embracing software-defined storage (SDS) for improved scalability.
Decoupling Hardware and Software
HPE’s strategy of decoupling hardware from software to stay adaptable to new technologies (e.g., NVMe fabrics, CPUs).
This allows for independent scaling of compute and storage, giving customers more flexibility.
11:00 -15:00 Scale and Efficiency
Granular Scalability and Cost Efficiency
Granular scalability allows customers to add storage or controllers incrementally, preventing overprovisioning and optimizing costs.
This OPEX-based approach enables customers to pay only for the resources they need at a given time, improving TCO (Total Cost of Ownership).
Supply Chain Efficiency and Modular Hardware
HPE’s standardized hardware platform, using non-proprietary components (e.g., AMD CPUs, OCP NICs), ensures quicker adoption of new technologies like Gen 5 SSDs.
The modular, Lego-like design allows customers to integrate new tech without replacing entire systems.
15:00 -22:00 Availability and Performance
High Availability and Performance Improvements
HPE’s stateless controllers ensure seamless failover, offering high availability and performance improvements via the NVMe fabric.
Customers benefit from dynamic scaling and minimal downtime, crucial for mission-critical workloads.
Investment Protection through Continuous Upgrades
Timeless program: HPE ensures that as newer CPUs, memory, and I/O modules are released, they can be integrated seamlessly without requiring a complete system replacement.
This offers customers future-proofing and investment protection for their infrastructure.
22:00 -26:00 Ability to adapt new technologies
Adapting to New Technologies and AI-Driven Management
HPE’s architecture enables rapid adoption of new technologies (e.g., Gen 5 SSDs, DDR5, CXL).
AI Ops simplifies operations by automating routine tasks such as provisioning, monitoring, and optimizing workloads. This reduces manual effort and speeds up processes.
Dynamic Workload Management
The system leverages AI to dynamically adjust infrastructure resources and Kubernetes policies based on workload needs, ensuring SLA compliance and consistent performance.
26:00 -30:00 Unified Hybrid Cloud Control
Seamless Hybrid Cloud Management
HPE’s architecture supports seamless hybrid cloud management, allowing customers to control both on-premises and cloud-based resources from a single platform.
This provides true hybrid cloud flexibility, enabling consistent governance and security across environments.
Operational Simplicity and Time Savings
The architecture reduces the time required to provision and manage storage from weeks to minutes.
With fewer classically trained storage admins, HPE’s platform is easy to use for IT teams, making management more efficient.
30:00 -34:00 Integrating GreenLake
GreenLake Integration and Service Expansion
HPE’s GreenLake Cloud Console enables customers to expand their storage services easily, starting with block storage and adding more services like data protection or AI services as needed.
GreenLake offers a scalable and flexible management platform for customers to adapt their hybrid cloud strategies.
Reimagining Enterprise Storage
HPE’s shift from traditional SAN to a universal platform offers greater flexibility and efficiency.
This approach caters to traditional IT workloads and modern cloud-native applications, meeting a wide range of enterprise needs.
34:00 -40:00 Wrap Up
Customer Success and Scalability
Strong customer adoption of Alletra MP, especially among large enterprises looking to standardize on HPE’s platform.
HPE’s system’s modularity allows for seamless scaling, whether for compute or storage, as customer needs evolve.
Future Innovation and Flexibility
HPE continues to innovate with a robust roadmap, promising more functionality and scalability in the near future.
The focus remains on future-proofing customer infrastructure and addressing dynamic, cloud-native workloads.
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42min

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