Presented by Solidigum
As AI increases adoption, data centers face a significant obstacle to storage – and traditional HDDs are at its center. Data that are once sitting as useless as cold archives are now pulling over and over repeated use to create more accurate models and provide better style results. This change from cold data to hot data demands low delays, high -throw storage that can handle parallel computers. HDD will remain work for low -cost cold storage, but without re -considering their role, the risk of high -capacity storage layer becomes the weakest link in the AI factory.
"Together with modern AI workload, data center barriers, has created new challenges for HDD," IDC Research Vice President Jeff Janokoches says. "Although HDD suppliers are focusing on increasing data storage by offering large drives, it often comes at a slow performance cost. As a result, the concept of ‘SSDS’ nearby is becoming a rapidly related topic of debate within the industry."
Today, AI operators need to maximize the use of GPUs, manage network-linked storage effectively and calculate scale-while rapidly reduce shortage and costs. In an environment where every watt and every square inches is calculated, Roger Koral, senior director of AI and Leadership Marketing in Soligam, says success needs more than technical refreshing. It has demanded a deep recovery.
“It speaks of a tectonic shift in the value of data for AI,” says Corrial. Along with the ability, they also bring performance and performance.
High-capable SSDs are not just displaceing HDD-they are removing one of the biggest obstacles on the AI factory floor. By providing widespread benefits in performance, performance and density, free the SSDGPU scale to further advance the power and space to further advance the scale. This is a less storage upgrade than structural change in which data infrastructure is designed for the AI era.
HDDS vs SDD: More than just a hardware refresh
HDD has impressive mechanical designs, but they consist of many dynamic parts that use more energy on a scale, take more space, and fail at a rate higher than solid state drives. Depending on spinning platters and mechanical reading/writing ends naturally limits input/output operations per second, which creates Backs barriers to AI workloads that demand low delays, high harmony, and retention thropped.
HDD also struggles with lateness sensitive tasks, as the physical process of data acquisition is disqualified to mechanical delays for real -time AI diagnosis and training. In addition, their strength and cooling requirements are repeatedly increased and significantly increases under access to extreme data, which reduces the scales of data and the status of heat.
In contrast, SSD -based wide storage solution reduces annual energy use M $ 1 million, and in AI environment where every watt is important, this is a huge advantage for SSD. To demonstrate this, Salim and extensive data completed a study that examined the data storage economics on the Exhibit Scale-one quadrilateral bytes, or a billion gigabytes, which analyzed storage power consumption over a period of 10 years.
As an initial reference point, you will need four 30 TBDDs equivalent to the same 122TB slygam SSD capacity. After factoring in widespread data reduction techniques through high performance of SSD, the Xibite solution includes 3,738 Solodigam SSD vs. 40,000 high -capacity HDD. The study says that a wide SSD -based solution uses 77 % less storage energy.
Minimizing the signs of the foot center of the data center
"We’re sending 122 Terbite drives to some of the above OEMS and leading AI cloud service providers in the world," Corel says. "When you compare Hybrid HDD + TLCSD Configure to All -122 TBSD, they are getting a savings under the influence of the data center. And yes, it is important in large -scale data centers that are making their nuclear reactors with renewable energy providers and signing heavy power procurement contracts, but it is important to reach your regional data centers, local data centers, and to your edge."
That is beyond nine to a savings space and power. This allows organizations to fit the infrastructure in the first available locations, increase the GPU scale, or create small feet marks.
"If you have been given the X -X money and the amount of electricity, you are going to use it. You are Ai" Corrial explains, “Where every watt and square inches is calculated, so why not use it very effectively? Get the most efficient storage on the planet and enable the maximum GPU scale within the envelope that you have to fit."
Another often overlooked element, the major physical image (too much) of data stored on mechanical HDDS results in maximum construction material impressions. Collectively, concrete and steel production is more than 15 % of global greenhouse gas emissions. Reducing the physical image of storage, can help reduce statue concrete and steel -based emissions by more than 80 % compared to high -capacity SSDHDS. And in the final stage of sustainable life cycle, which is at the end of life, there will be 90 % less drive. .
Changing cold and archive storage strategies
The SDD move is not just a storage upgrade. This is a fundamental status of data infrastructure strategy in the AI era, and its pace is increasing.
"بڑے ہائپرسکلرز اپنے موجودہ انفراسٹرکچر سے زیادہ سے زیادہ فائدہ اٹھانا چاہتے ہیں ، غیر فطری حرکتیں کرتے ہیں ، اگر آپ کریں گے ، اگر آپ کریں گے تو ، ایچ ڈی ڈی کے ساتھ ، ان کو زیادہ سے زیادہ 90 فیصد تک پہنچنے کی کوشش کی جا رہی ہے تاکہ ہر ٹیرا بائٹ کو زیادہ سے زیادہ آئی او پی ایس کو ختم کرنے کی کوشش کی جاسکے ، لیکن وہ آس پاس آنے لگے ہیں ، لیکن وہ آس پاس آنے لگے ہیں ،" Corel says. "Once they turn to all modern high -capacity storage infrastructure, the large -scale industry will be at this pace. In addition, we are starting to look at the lessons that apply to other classes learned on the value of modern storage in AI, such as Big Data Analytics, HPC, and many."
He added that although all -flush solutions are almost acceptable globally, there will always be a place for HDD. HDD will be used in use such as archives, cold storage, and scenarios, where per gigabytes concerns are much higher than the need to access real -time at a net cost. But as the token economy gets heated and businesses have a sense of value to monitor the data, the segments of hot and warming data will continue to increase.
To solve the challenges of future power
Now in its fourth generation, with more than 122 overall xbiditis, has been shipped to this day, the QLC (quad level cell) technology of Soldigam has led the industry to balance high drive capabilities with cost performance.
"We don’t think about storage as we only store bits and bytes. We think about how we can develop amazing drives that are eligible to provide benefits at the level of solution," Corel says. "The shining star on it has recently been launched, E1.S, especially for the next generation chain -lace GPU server directly designed for dense and efficient storage attached storage configuration."
Solidigam D7-PS1010 E1.S is a breakthrough, the first ESD in the industry with a single-sided chip liquid cooling technology. Suligam worked with NVIDIA to tackle the dual challenges of heat management and cost performance, while providing high performance needed to demand AI workload.
"We are quickly moving towards an environment where all the main components of IT will be directly cool from the directly on the sidewalk on the sidewalk," He says. "I think the market needs to look at their view of cooling, because the limits of power, the challenges of power are not going to end in my life. They need to apply a nuclear mentality to how they are making the most effective infrastructure."
The rapidly complex estimates are pushing against the wall of memory, which makes storage architecture a frontline design challenge, not thinking later. High -capacity SSDs, which are folded with liquid cooling and efficient design, are emerging as the only way to meet the growing demands of AI. Now the mandate is not only to build the infrastructure for performance, but also the storage that can effectively measure the data. Now the organizations that will ensure the storage will be the only ones that will be able to scale AI tomorrow.
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