Title: Hi HN,<p>We are Sergiy, Davit and Jason, founders of Snark AI (<a href="https:&#x2F;&#x2F;snark.ai" rel="nofollow">https:&#x2F;&#x2F;snark.ai</a>). We provide low-cost GPUs for Deep Learning training and deployment on semi-decentralized servers.<p>We started Snark AI during our PhD programs at Princeton University. As deep learning researchers we always experienced lack of GPU resources. Renting out GPUs on the cloud didn&#x27;t fit in our budget, and purchasing GPU cards was difficult -- at that time, so many GPUs were being taken away by the crypto-miners. Then we found out that GPU mining profits lag far behind public cloud GPU prices.<p>On top of that, we figured out that there&#x27;s a way to run Neural Network inference and crypto-mining simultaneously without hurting mining hash rate. This observation is a little counterintuitive, but it turns out that anti-asic hashing algorithms are designed to be extremely memory intensive, which leaves a good chunk of the CUDA cores idle. We can utilize the leftover compute power to run Neural Network inference extremely cost efficiently, which could be a life savior for large-scale inference tasks. <a href="http:&#x2F;&#x2F;snark.ai&#x2F;blog" rel="nofollow">http:&#x2F;&#x2F;snark.ai&#x2F;blog</a><p>At the same time, we provide low cost raw hardware access for Neural Network training. We aim to be up to 10 times cheaper than on-demand instances on public cloud, undercutting preempteble&#x2F;spot instance by up to 2x. When the GPU is idle our algorithms efficiently switch to mining to reduce costs. Try it out at <a href="https:&#x2F;&#x2F;lab.snark.ai" rel="nofollow">https:&#x2F;&#x2F;lab.snark.ai</a>, with 10 hours of free GPU time. We made it very simple to access the hardware through a single command line after `pip3 install snark`. More information on usage here <a href="https:&#x2F;&#x2F;github.com&#x2F;snarkai&#x2F;snark-doc" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;snarkai&#x2F;snark-doc</a>. We are also working on creating a hub for NNs, similar to docker hub. It is still work in progress but you can take a look at couple examples at <a href="https:&#x2F;&#x2F;hub.snark.ai&#x2F;explore" rel="nofollow">https:&#x2F;&#x2F;hub.snark.ai&#x2F;explore</a>.<p>We would love to get your feedback, to understand how was the experience for training Deep Networks through our platform and then deploying.
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