At a keynote at the GPU Technology Conference in Munich today, Nvidia, the video/graphics company turned Artificial Intelligence (AI) juggernaut, is today going another step forward in the AI direction. This time though, Nvidia isn't announcing a new Graphics Processing Unit (GPU) platform, or a new proprietary SDK for deep learning, but is instead announcing new a set of new open source libraries for GPU-accelerated analytics and machine learning (ML). Rapid AI movement Dubbed RAPIDS, the new library set will offer Python interfaces similar to those provided by Scikit Learn and Pandas, but which will leverage the company's CUDA platform for acceleration across one or multiple GPUs. According to Nvidia CEO Jensen Huang, who briefed a number of technology journalists by phone on Tuesday, Nvidia has seen 50x speed up in training times when using RAPIDS versus a CPU-only implementation. Integrations and partners RAPIDS apparently incorporates in-memory columnar data technology Apache Arrow, and is designed to run on Apache Spark.
Nvidia has taken the wraps off its next iteration of workstations for data scientists and users interested in machine learning, with a reference design featuring a pair of Quadro RTX GPUs. Announced at Nvidia GTC on Monday, the dual Quadro RTX 8000 or 6000 GPU design is slated to provide 260 teraflops, and have 96GB of memory available thanks to the use of NVLink. Signed up to provide the new, beefier workstations are Dell, HP, and Lenovo. On the server side, the company unveiled its RTX blade server, which can pack 40 GPUs into an 8U space, and is labelled as a RTX Server Pod when combined with 31 other RTX blade servers. The storage and networking backbone of the blade servers are provided by Mellanox -- which Nvidia purchased just shy of $7 billion last week.
Last week we wrapped up a highly successful GPU Technology Conference (GTC) Europe in Munich! GTC is NVIDIA's international conference series, bringing together the top minds in deep learning, analytics, and of course GPUs for sessions, workshops, keynotes, and more. This was the place to be for any and all European organizations interested in leveraging the power of the GPU. As the Kinetica engine runs on GPUs, there's no better place for us to share our solutions for advanced analytics and deep learning. This year we noticed a significant increase in the number of organizations that understand the challenges of the Extreme Data Economy.
John started his career in the Financial Services sector with Sun Microsystems, managing global relationships with JP Morgan & Bank of America. He went on to spend 10 years at NetApp, leading global FSI teams and then two years at Juniper Networks, as EMEA Sales Director for FSI. Today, he is responsible for NVIDIA's FSI business in EMEA, along with the newly announced RAPIDS machine learning business in EMEA.