Voltron Data just partnered with Accenture to solve one of AI’s biggest headaches

An unprecedented demand for artificial intelligence data processing, drive to the beginning of the mountain vision Voltron Information EU has the least discussed but offers a solution to one of the most critical problems: moves quickly enough to quickly save databases quickly.

Voltron information that declares a strategic partnership with Mamur Today, the accelerated analytical engine in a GPU that can help enterprises. The main product of the company, ThisThe organization allows traditional computer processors (CPUs) instead of processing Petabyte scale data using graphical processing sections (GPU).

“Everyone is directed to new items that will be able to touch and feel, but it will be the key, the key to VentureBeat is the exclusive interview with VentureBeat, the exclusive interview is in the exclusive interview, Accenture’s Bank and Capital Markets experience.” To do the work of AI, never You have to move around a speed and tempo that time has never been before. “

Building for AI Tsunami: Why traditional data processing will not cut it

Partnership, Generative AI’s hurried companies find their existence Information infrastructure Not equipped to manage the volume and speed of the required information. The AI ​​agents of this problem are expected to strengthen because it spreads more in business operations.

“Agents will probably write SQL inquiries than people in a very short time,” he said. “If CIOS and CTOs have already spent a lot of time on informational analytics and cloud infrastructure, we need a step function for the operation of these conspiracy.”

Unlike traditional database vendors who returned GPU support to existing systems, Voltron has set up the GPU in acceleration. “When they are trying to accelerate GPU, most companies will monitor GPU over an existing system,” he said. “By setting up the ground … Take 10x, 20x, 100x, depending on the performance profile of a particular workload.”

From 1400 servers to 14: Early adopts are looking for dramatic results

Company duties This Using the Apache Arrow frame for quick data movements, in accordance with the platforms built as snow flaps and platforms in data. “More than competition is a accelerator to all these databases,” Abbott said. “It still uses the same SQL written to get the same answer, but get something faster and faster in parallel.”

Early adoption, information is directed to the intensive industry of data, where there are fraud detection, risk modeling and regulatory compatibility. According to Aramburu, a large retail seller from 1400 CPU machines after implementing these streams decreased only to 1400 CPUs.

Since the beginning Nvidia’s GTC Conference The last March, Voltron information, gave information about 14 enterprises customers, including the two major government agencies. The company plans to leave the “test driver” version that allows potential customers to test the Terabyte database in GPU with accelerated surveys.

To turn GPU deficiency into an opportunity

Current GPU deficiency The spark with AI requirement is useful for both difficult and voltron data. While taking new placements, the GPU infrastructure purchased for the AI ​​business loading for many institutions first purchased for the assets purchased for data processing.

“We’ve seen it as a Boon in advance, before that before this before, it’s not there that there are many GPUs,” he said.

Technology Abbott is aware of the “trapped data” and is difficult to be in the formats that may be valuable for AI education. “Some information that Voltro will show to you, using this technology, using this technology, you have been potentially 90% more effective and efficient to use this technology,” Abbott said. “This is the power.”

An entity is increasingly critical with the increase in solutions, solutions, which can reduce the information requirements, data development and infrastructure costs. Accenture’s partnership, Voltron information, looking at these difficulties, can help more organizations that allow these difficulties to significantly improve the activities and efficiency of AI initiatives.