Linode, a cloud hosting provider headquartered in Philadelphia with a global network of 11 data centers, is expanding services to its “fast-growing” APAC customer base by adding GPU instances to its Singapore data center.

The new capability would give users in the region a “low-latency” option for “near-real-time” data analytics, image processing, video transcoding, artificial intelligence (AI) and other data and processing intensive applications. The Singapore data center joins Newark and Mumbai as the third of Linode’s global data centers to offer GPU instance types.

Linode GPU instances are completely dedicated. The GPUs are powered by the NVIDIA Quadro RTX 6000 Graphics Card, an efficient and versatile rendering solution for virtual workstations in the cloud with a versatile combination of CUDA, Tensor and RT cores. Linode’s Quadro RTX line of GPUs are running NVIDIA’s Turing GPU architecture.

‘Affordable GPU Instances’

Photo Alex Peña, lead research and development engineer at Linode
“Adding GPUs to our Singapore data center means that innovators in the region now have access to affordable GPU instances, with the additional benefits of low latency,” said Alex Peña, lead research and development engineer at Linode.

“More and more, Linode’s customers are looking to us for advanced cloud services like GPU instances, and they need those services close to their end users,” said Alex Peña, lead research and development engineer at Linode. “Adding GPUs to our Singapore data center means that innovators in the region now have access to affordable GPU instances, with the additional benefits of low latency. Linode is dedicated to making GPU technology accessible and affordable so more developers can access the power they need to innovate faster.”

Linode GPUs are offered in four tiers of card capacity and price, allowing customers to select the tier that best fits their needs. The virtual machine GPU instances are intended to give scientists, artists, and engineers working on artificial intelligence, graphic visualization, and complex modeling a “cost-efficient” alternative to hyperscale cloud providers.

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