<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>VWWHFSfQ</author><text>Haha!  Back in ~2014 or so my company was spending nearly $30,000&amp;#x2F;month on an EC2 &amp;quot;compute-optimized&amp;quot; cluster to transcode live video streams to multiple renditions.  One of our engineers said hey, why don&amp;#x27;t we try to colo some real hardware?  We did a test with a single bare-metal 8-core Xeon server and it completely destroyed the performance of the EC2 &amp;quot;compute-optimized&amp;quot; cluster!&lt;p&gt;After that we colo&amp;#x27;d 4 big Xeon servers for about $1,600&amp;#x2F;month total.  Looking back on it now it&amp;#x27;s just so insane...$30,000?  no way.</text><parent_chain><item><author>CapmCrackaWaka</author><text>&amp;gt; Benchmarking cutting-edge graph-processing algorithms running on 128-core clusters against a single-threaded 2014 Macbook Pro. The laptop consistently wins, sometimes by an order of magnitude.&lt;p&gt;LOL, this hits close to home. My company had a modeling specific VM set up to run our predictive modeling pipelines. Typical pipeline is about 50,000 to 5 million rows of training data. At best, using an expensive VM, we managed to get 2x training speed from lightgbm on the VM vs my personal work laptop. We tried GPU boxes, hyper threaded machines, you name it. At the end of the day, we decided to let our data scientists just run models locally.</text></item></parent_chain></comment><story><title>Cold Showers</title><url>https://github.com/hwayne/awesome-cold-showers</url></story>
<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>thom</author><text>There appear to be slightly weird commercial reasons behind this, because gaming GPUs have great CUDA performance but NVIDIA won’t let you put them in a datacentre. So buying your data scientists gaming laptops (RGB and all) generally works out faster for any reasonable price point. That said, a dedicated server with a decent Xeon and MKL set up correctly generally outperforms CPU-bound stuff.</text><parent_chain><item><author>CapmCrackaWaka</author><text>&amp;gt; Benchmarking cutting-edge graph-processing algorithms running on 128-core clusters against a single-threaded 2014 Macbook Pro. The laptop consistently wins, sometimes by an order of magnitude.&lt;p&gt;LOL, this hits close to home. My company had a modeling specific VM set up to run our predictive modeling pipelines. Typical pipeline is about 50,000 to 5 million rows of training data. At best, using an expensive VM, we managed to get 2x training speed from lightgbm on the VM vs my personal work laptop. We tried GPU boxes, hyper threaded machines, you name it. At the end of the day, we decided to let our data scientists just run models locally.</text></item></parent_chain></comment><story><title>Cold Showers</title><url>https://github.com/hwayne/awesome-cold-showers</url></story>