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NVIDIA is attempting to separate enterprise/datacenter and consumer chips to justify the cost disparity. Specifically, they're introducing memory, precision etc. limits which have major performance implications to GeForce and there's also the EULA which was been mentioned here. That said, everything AWS comes at a premium as they're making the case that on-demand scale outweighs the pain of management/CapEx. This premium is especially noticeable with more expensive gear like GPUs. At Paperspace (https://paperspace.com), we're doing everything we can to bring the cost down of cloud and in particular, the cost of delivering a GPU. Not all cloud providers are the same :)

Disclosure: I work on Paperspace

3 comments

Paperspace is great for the price(especially for storage) and easy for deployment. However, the customer support is horrible. Turnaround time is around a week, and sometimes they seem to have billing issues. One time I got charged around $100 for trying out a GPU machine for 2 hours(which is the number of hours showed up on the invoice), and it took them three weeks to eventually issue a refund. That made me decide to switch. Hope you guys can hire more people for customer support or have better solution for issues like this.

I contemplated building my own machine for deep learning. I'm a geek so I do like building hardware. But I couldn't even justify spending $2k on a deep learning machine.

Paperspace has eliminated my desire to build a deep learning computer thanks to their insanely low prices.

Current prices are around $0.78 per hour for a Nvidia Quadro P5000, that's pretty comparable to a 1080 TI.

On top of that you can even run Gradient notebooks (on demand) without even setting up a server. This is the future when bandwidth costs are minimal: thin clients, powerful servers.

At the end of the day, I wanted to spend more time tuning the ML pipeline rather than fussing with drivers, OS dependencies, etc

Sure there's lots of things that Paperspace could do better, but their existing product is already leaps and bounds better than GCloud or AWS. AWS and GCloud wins through big contracts with large businesses and I'm just a little guy.

Disclosure: I do not work for Paperspace and am not paid to endorse them in any way. I love their product.

What are those performance limitations, really?

Memory? Because if you can spread your model across multiple GPUs, and you've implemented Krizhevsky's One Weird Trick to switch between reducing the smallest of either parameters or deltas, you're golden.

I thought tensor cores and NVLINK would end up Tesla differentiators, and really great ones at that, but now they're both in the Turing consumer GPUs so I am really scratching my head here.

That said, the EULA is just stupid. I cannot use CUDA 9.2 or later at work because of it. No one is going to audit our computers for any reason ever, period, full stop.