IBM will adopt NVIDIA GPU accelerator on the cloud to boost AI workloads

IBM announced this week that it is the initial global cloud provider to make the NVIDIA Tesla P100 GPU accelerator available on the cloud. By combining NVIDIA’s acceleration technology with IBM’s Cloud platform, businesses can expect to run compute-heavy workloads, such as artificial intelligence, deep learning and high performance data analytics.

As a cloud provider that enables the use of the Tesla P100 GPU accelerator on the cloud, IBM will make it more accessible for businesses in industries including healthcare, financial services, energy and manufacturing to extract valuable insight from big data.

For example, clients in the financial services industry can use GPUs on the IBM Cloud to run complex risk calculations; healthcare companies can analyze data or identify possible genetic variations; and energy companies can identify new ways to improve operations.

In a cloud environment, GPUs work in conjunction with a server’s CPU to accelerate application and processing performance. The CPU offloads compute-intensive portions of the application to the GPU, which processes large blocks of data at one time rather than sequentially boosting the overall performance in a server environment.

“The latest NVIDIA GPU technology delivered on the IBM Cloud is opening the door for enterprises of all sizes to use cognitive and AI to address complex big data challenges,” said John Considine, general manager, Cloud Infrastructure, IBM. “IBM’s global network of cloud data centers along with its advanced cognitive and GPU capabilities is helping to accelerate the pace of client innovation.”

“As the AI era takes hold, demand continues to surge for our GPU-accelerated computing platform in the cloud,” said Ian Buck, general manager, Accelerated Computing, NVIDIA. “These new IBM Cloud offerings will provide users with near-instant access to the most powerful GPU technologies to date – enabling them to create applications to address complex problems that were once unsolvable.”

Clients will have the option to equip individual IBM Bluemix bare metal cloud servers with two NVIDIA Tesla P100 accelerator cards. The Tesla P100 provides 4.7 teraFLOPS of double-precision performance and 16 gigabytes of GPU memory in a single server to accelerate compute-intensive workloads.

The combination of IBM’s connectivity and bare metal servers with the Tesla P100 GPUs provides clients with higher throughput than traditional virtualized servers. This high level of performance can allow clients to deploy fewer, more powerful cloud servers to deliver increasingly complex simulations and big data workloads.

With more than 160 zettabytes of data expected to be generated by 2025 according to IDC[1], enterprises of all sizes are increasingly relying on cognitive and deep learning applications to make sense of these massive volumes and varieties of data. GPUs, or graphics processing units, work in conjunction with a server’s central processing unit (CPU) to accelerate application performance helping these compute-heavy data workloads run more quickly and efficiently.

Since 2014, IBM has been working closely with NVIDIA to bring the latest GPU technology to the cloud. IBM was the first cloud provider to introduce the NVIDIA Tesla K80 GPU accelerator in 2015 and the Tesla M60 in 2016, and the introduction of the Tesla P100 builds on IBM’s position in bringing the latest NVIDIA GPU technology to the cloud for machine learning, AI and High Performance Computing (HPC) workloads.

“Deep learning and AI was once reserved for a select few companies such as research organizations and universities,” said Subbu Rama, founder and CEO, Bitfusion. “By collaborating with IBM to use NVIDIA GPUs on the cloud, BitFusion is changing that dynamic, enabling data scientists and developers to jumpstart innovation.”

The Tesla P100 joins NVIDIA’s portfolio of GPU offerings on the IBM Cloud, including the Tesla M60, Tesla K80 and Tesla K2 GPUs. IBM plans to make the Tesla P100 GPU accelerators available this month.


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