The Looming Battle Over AI Chips
If you&#x27;re FB, GOOG, AAPL, AMZN, BIDU, etc, this strategy makes sense because much like they have siloed data, they also have siloed computation graphs for which they can lovingly design artisan transistors to make the perfect craft ASIC.  There&#x27;s big money in this.<p>Or you can be like BIDU, buy 100K consumer GPUs, and put them in your datacenter. In response, Jensen altered the CUDA 9.1 licensing agreement and the EULA for Titan V such that going forward, you cannot deploy Titan V in a datacenter for anything but mining cryptocurrency, and his company reserves the right going forward to audit your use of their SW and HW at any time to force compliance with whatever rules Jensen pulled out of his butt that day after his morning weed.  And that&#x27;s a shame.  Because there&#x27;s no way any of these companies can beat the !&#x2F;$ of consumer GPUs and NVDA is lying out of its a$$ to say you can&#x27;t do HPC on them.<p>But beyond NVDA shenanigans, I think it&#x27;s incredibly risky to second guess those siloed computation graphs from the outside in the hopes of anything but an acqui-hire for an internal effort.  Things ended well for Nervana even if their HW didn&#x27;t ship in time, but when I see a 2018 company (<a href="http:&#x2F;&#x2F;nearist.ai&#x2F;k-nn-benchmarks-part-wikipedia" rel="nofollow">http:&#x2F;&#x2F;nearist.ai&#x2F;k-nn-benchmarks-part-wikipedia</a>) comparing their unavailable powerpoint processor to GPUs from 2013, and then doubling down on doing so when someone rightly points out how stupid that is, I see a beached fail whale in the making, not a threat to NVDA&#x27;s Deepopoly.