Presented by Solidigm
As AI adoption increases, data centers are facing a critical storage shortage – and traditional hard drives are at the middle of it. Data that after sat idle as cold archives is now widely used to construct more accurate models and supply higher inference results. This shift from cold to warm data requires low-latency, high-throughput storage that may handle parallel computations. Hard drives will proceed to be the workhorse of low-cost cold storage, but without rethinking their role, the high-capacity storage layer risks becoming the weakest link within the AI factory.
“Modern AI workloads combined with data center constraints have created latest challenges for hard drives,” said Jeff Janukowicz, research vp at IDC. “While HDD vendors are responding to data storage growth by offering larger drives, this often comes on the expense of slower performance. As a result, the concept of 'nearline SSDs' is becoming an increasingly relevant topic of debate within the industry.”
Today, AI operators must maximize GPU utilization, efficiently manage network attached storage, and scale computing power—all while reducing costs as power and space grow to be increasingly scarce. In an environment where every watt and square inch counts, says Roger Corell, senior director of AI and leadership marketing at Solidigm, success requires greater than a technical refresh. It requires a deeper realignment.
“It shows the tectonic shift in the worth of information for AI,” says Corell. “This is where high-capacity SSDs come into play. In addition to capability, additionally they bring performance and efficiency – enabling exabyte-scale storage pipelines to maintain up with the relentless pace of information set size. All of this consumes power and space, so we’d like to make it as efficient as possible to enable greater GPU scaling on this constrained environment.”
High-capacity SSDs aren't just displacing HDDs – they're also eliminating one in every of the largest bottlenecks within the AI factory. By delivering huge increases in performance, efficiency and density, SSDs unlock the facility and space needed to further increase GPU scaling. This is less a storage upgrade and more a structural shift in designing the information infrastructure for the AI age.
Hard drives vs. SDDs: More than simply a hardware upgrade
Hard drives have a formidable mechanical design, but they consist of many moving parts that, on a big scale, use more energy, take up more room, and fail more often than solid-state drives. Reliance on spinning platters and mechanical read/write heads inherently limits input/output operations per second (IOPS), creating bottlenecks for AI workloads that require low latency, high concurrency, and sustained throughput.
Hard drives also struggle with latency-sensitive tasks, because the physical act of trying to find data introduces mechanical delays which can be unsuitable for real-time AI inference and training. Additionally, their power and cooling requirements increase significantly with frequent and intensive data access, reducing efficiency as the information scales and heats up.
In contrast, the SSD-based VAST storage solution reduces energy consumption by about $1 million per yr, and in an AI environment where every watt counts, it is a huge advantage for SSDs. To reveal this, Solidigm and VAST Data conducted a study examining the economics of information storage on the exabyte scale – a quadrillion bytes or a billion gigabytes – with an evaluation of storage power consumption in comparison with hard drives over a 10-year period.
As a place to begin, you'll need 4 30TB hard drives to succeed in the capability of a single 122TB Solidigm SSD. Utilizing VAST's data reduction techniques enabled by the superior performance of SSDs, the Exabyte solution includes 3,738 Solidigm SSDs versus over 40,000 high-capacity hard drives. The study found that the SSD-based VAST solution used 77% less storage energy.
Minimizing data center space requirements
“We supply 122 terabyte drives to a number of the top OEMs and leading AI cloud service providers worldwide,” says Corell. “Comparing a 122TB SSD to a hybrid HDD + TLC SSD configuration, it's a nine-to-one savings in data center footprint. And yes, that's vital in these massive data centers that construct their very own nuclear reactors and have large power purchase agreements with renewable energy providers, but it surely becomes increasingly vital while you go to the regional ones data centers, the local data centers and as much as your individual.” Edge deployments where space could also be at a premium.”
These nine-to-one savings transcend space and power – they allow corporations to deal with infrastructure in previously unavailable space, expand GPU scaling, or construct smaller footprints.
“If you're given Maintain storage shafts and thus also reduce costs.” related to it’s gone.”
Another often neglected element: the (much) larger physical footprint of information stored on mechanical hard drives ends in a bigger footprint for construction materials. Overall, concrete and steel production is answerable for over 15% of world greenhouse gas emissions. By reducing the physical footprint of storage, high-capacity SSDs can assist reduce concrete and steel emissions by greater than 80% in comparison with HDDs. And within the last phase of the sustainability life cycle, the end-of-life phase of the drive, 90% fewer drives will likely be required for disposal. .
Redesign cooling and archival storage strategies
Switching to SDD isn't only a storage upgrade; It's a fundamental shift in data infrastructure strategy within the age of AI, and it's gathering pace.
“Large hyperscalers are attempting to benefit from their existing infrastructure by committing unnatural acts, should you will, resembling over-sizing HDDs to almost 90% to squeeze out as many IOPS per terabyte as possible, but they’re beginning to catch on,” says Corell. “Once they move to a totally high-capacity modern storage infrastructure, the industry as a complete will likely be on that journey. Additionally, we’re beginning to see these insights in regards to the value of recent storage in AI being applied to other segments, resembling big data analytics, HPC, and plenty of more.”
While all-flash solutions are being adopted almost universally, there’ll at all times be a spot for hard drives, he adds. Hard drives persist in applications resembling archiving, cold storage, and scenarios where pure cost-per-gigabyte concerns outweigh the necessity for real-time access. But because the token economy heats up and firms realize the worth of monetizing data, the nice and cozy and warming data segments will proceed to grow.
Solving the energy challenges of the long run
Solidigm's QLC (Quad-Level Cell) technology is now in its 4th generation and has delivered a complete of greater than 122 exabytes thus far. This makes it an industry leader relating to balancing higher drive capacities with cost efficiency.
“We're not only taking a look at storage as storing bits and bytes. We're fascinated by how we will create these amazing drives that may provide solution-level advantages,” says Corell. “The shining star here is our recently launched E1.S, designed specifically for dense and efficient storage in direct-attach storage configurations for the next-generation fanless GPU server.”
The Solidigm D7-PS1010 E1.S is a breakthrough, the industry's first eSSD with single-sided direct-to-chip liquid cooling technology. Solidigm worked with NVIDIA to handle the twin challenges of thermal management and value efficiency while delivering the high performance required for demanding AI workloads.
“We are quickly moving to an environment where all critical IT components on the direct attach side are liquid cooled on to the chip,” he says. “I feel the market must rethink its cooling approach because performance constraints and energy challenges should not going to go away, at the very least in my lifetime. They must adopt a neocloud mindset to design essentially the most efficient infrastructure.”
Increasingly complex reasoning hits a memory wall, making memory architecture a front-line design challenge fairly than an afterthought. High-capacity SSDs, coupled with liquid cooling and efficient design, are proving to be the one solution to meet the increasing demands of AI. The task now could be to construct an infrastructure that not only serves efficiency, but additionally provides storage that might be efficiently scaled as data volumes grow. The corporations that repurpose storage now can even have the ability to scale AI tomorrow.
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