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Plan to bring capabilities to data scientists everywhere
By 2020, we anticipate that the world’s volume of digital data will exceed 44 zettabytes, an astounding number. As enterprises begin to realize the vast, untapped potential of data, they need to find a way to exploit it. Enter AI.
IBM has worked to build the industry’s most complete data science platform. Integrated with NVIDIA GPUs and software designed specifically for AI and the most data-intensive workloads, IBM has infused AI into offerings that clients can access regardless of their deployment model.
Today, we take the next step in that journey in announcing the next evolution of our collaboration with NVIDIA. We plan to leverage their new data science toolkit, RAPIDS, across our portfolio so that our clients can enhance the performance of machine learning and data analytics.
This includes planning to bring GPU-accelerated machine learning to[i]:
- IBM POWER9 with PowerAI: to leverage RAPIDS to expand the options available to data scientists with new open source machine learning and analytics libraries. Accelerated workloads have been proven to get a direct benefit from the exclusive engineering that NVIDIA and IBM have done around POWER9, including integration of NVIDIA NVLink and NVIDIA Tesla GPUs. PowerAI is IBM’s software layer, which optimizes how today’s data science and AI workloads run on these heterogeneous computing systems. Our goal is for this improved performance trajectory for GPU-accelerated workloads on POWER9 to continue with RAPIDS.
- IBM Watson Studio and IBM Watson Machine Learning: to take advantage of the power of NVIDIA GPUs so that data scientists and AI developers can build, deploy, and run faster models than CPU-only deployments in their AI applications in a multicloud environment with IBM Cloud Private for Data and IBM Cloud.
- IBM Cloud: to users who choose machines equipped with GPUs so they can apply accelerated machine learning and analytics libraries in RAPIDS to their cloud applications and tap into the benefits of machine learning.
“IBM and NVIDIA’s close collaboration over the years has helped leading enterprises and organizations around the world tackle some of the world’s largest problems,” said Ian Buck, vice president and general manager of Accelerated Computing at NVIDIA. “Now, with IBM taking advantage of RAPIDS open-source libraries announced today by NVIDIA, GPU accelerated machine learning is coming to data scientists, helping them analyze big data for insights faster than ever possible before.”
Recognizing the computing power that AI would need, IBM was an early advocate of data-centric systems. This approach led us to deliver the GPU-equipped Summit system, the world’s most powerful supercomputer, and already researchers are seeing tremendous returns. Earlier in the year, we demonstrated the potential for GPUs to accelerate machine learning when we showed how GPU-accelerated machine learning on IBM Power Systems AC922 servers set a new speed record with a 46x improvement over previous results.
Because of IBM’s commitment to bringing accelerated AI to users across the technology spectrum, be they users of on-premises, public cloud, private cloud, or hybrid cloud environments, we are the only vendor that is positioned to bring RAPIDS to users regardless of how they want to access them. It’s for this reason that NVIDIA has chosen IBM as a signature provider of RAPIDS.
We are excited to see the impact that these new tools will have for our clients.
[i] Statements regarding IBM’s future direction and intent are subject to change or withdrawal without notice and represent goals and objectives only.