Baidu Eyes Deep Learning Strategy in Wake of New GPU Options

This month Nvidia bolstered its GPU strategy to stretch further into deep learning, high performance computing, and other markets, and while there are new options to consider, particularly for the machine learning set, it is useful to understand what these new arrays of chips and capabilities mean for users at scale. As one of the companies directly in the lens for Nvidia with its recent wave of deep learning libraries and GPUs, Baidu has keen insight into what might tip the architectural scales--and what might still stay the same, at least for now. Back in December, when we talked to one of the lead scientists at Baidu's Silicon Valley AI Lab, Bryan Catanzaro, we dug into how teams there make architectural decisions to power deep learning for speech recognition and other services. At the time, he told us about their use of Nvidia Titan X GPU cards as the most cost efficient option for the computationally-intensive task of model training, despite the availability of other GPUs, including the M40 and for the inference phase, M4 as well as other more powerful GPUs, including the supercomputing oriented Tesla K80. Following GTC16, where Nvidia announced its forthcoming Pascal architecture, yet another possible option for these workloads emerged in the form of the P100, which have detailed rather extensively here and here.