<story><title>Nvidia  CEO Reveals New TITAN X at Stanford Deep Learning Meetup</title><url>https://blogs.nvidia.com/blog/2016/07/21/titan-x/</url></story><parent_chain></parent_chain><comment><author>cs702</author><text>Great news all-around for deep learning practitioners.&lt;p&gt;Nvidia says memory bandwidth is &amp;quot;460GB&amp;#x2F;s,&amp;quot; which will probably have the most impact on deep learning applications (lots of giant matrices must be fetched from memory, repeatedly, for extended periods of time).  For comparison, the GTX 1080&amp;#x27;s memory bandwidth is quoted as &amp;quot;320GB&amp;#x2F;s.&amp;quot;  The new Titan X also has 3,584 CUDA cores (1.3x more than GTX 1080) and 12GB of RAM (1.5x more than GTX 1080).&lt;p&gt;We&amp;#x27;ll have to wait for benchmarks, but based on specs, this new Titan X looks like the best single GPU card you can buy for deep learning today.  For certain deep learning applications, if properly configured, two GTX 1080&amp;#x27;s might outperform the Titan X and cost about the same, but that&amp;#x27;s not an apples-to-apples comparison.&lt;p&gt;A beefy desktop computer with four of these Titan X&amp;#x27;s will have 44 Teraflops of raw computing power, about &amp;quot;one fifth&amp;quot; of the raw computing power of the world&amp;#x27;s current 500th most powerful supercomputer.[1]  While those 44 Teraflops are usable only for certain kinds of applications (involving 32-bit floating point linear algebra operations), the figure is still kind of incredible.&lt;p&gt;[1] &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;www.top500.org&amp;#x2F;list&amp;#x2F;2015&amp;#x2F;06&amp;#x2F;?page=5&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;www.top500.org&amp;#x2F;list&amp;#x2F;2015&amp;#x2F;06&amp;#x2F;?page=5&lt;/a&gt;</text></comment>
<story><title>Nvidia  CEO Reveals New TITAN X at Stanford Deep Learning Meetup</title><url>https://blogs.nvidia.com/blog/2016/07/21/titan-x/</url></story><parent_chain></parent_chain><comment><author>tylerwhipple</author><text>As great as this product may be, I went to the Stanford Deep Learning Meetup to learn more about how Baidu Research&amp;#x2F;Andrew Ng are solving large scale deep learning problems. I am disappointed by how much (unannounced) time was dedicated to the keynote&amp;#x2F;sales pitch.</text></comment>