With that money ("for a couple of days"), you could buy your own server and do it yourself, for a fraction of the price. And don't tell me you can't find people who'd be capable of doing that, if you've got AI development going on.
What am I missing?
From the tweet thread, it seems like there was some misunderstanding over where the files are being stored and being executed. This is a pretty common issue with Google Drive. I.e. if someone shares a file with me, and I copy it to a folder, it's just a pointer to the original file. Only after clicking "Add to My Drive" does it count against my storage allocation, and only then is it a distinct copy.
My guess is that the researchers expected each user to be able to run the game in their own personal free Colab environment, not be running it against the university's compute and storage budget.
One of the reasons such AI tech is able to move this fast because the cloud has become a commodity. People start to expect this kind of flexibility. It's impossible to self-host every possible thing some guy might want to try out.
Once you have a stable application - sure you might want to invest in own hosting for this, but even then it's an expensive up-front investment for something that might not go anywhere. I know a few places where they have a bunch of maxed-out nVidia DGX systems catching dust for exactly this reason.
10k/day makes sense for a short project, but if this is going to be an on-going thing -- like 2-3 years -- then absolutely buy that hardware and have an internal team run it.
We (those who read HN) could probably download the code and run it at home, but I think the authors want non-technical users to play. To do that, they need an accessible Python runtime, so they're hosting the game in a Colab notebook. The download in question is referring to downloading the weights of the neural net into the VM running the notebook.
If only redistributing Python apps wasn't so difficult.
I think that's why the author was serving the game through Colab since the majority of users probably don't have a 12GB GPU.
As I have demonstrated, I've really not much of a clue when it comes to AI, but do users really need 12Gb GPU RAM, 100% of the time? Maybe it's possible to use one GPU for multiple users?
60k users yesterday * 6GB each -> 360TB of data egress!
Normally, a scenario like this wouldn't involve bandwidth costs because GCP -> GCP same-region bandwidth is free, but Colab is technically not part of GCP, so the bandwidth charge is being assessed as egress to the public internet, which is pricy for that much data. Though it's probably still a lot cheaper than paying for the GPU-hours for that many users.
In other words, yes, this is an amazing amount of raw power - with a corresponding price tag.
EDIT: Almost, the VM runs on Colab, which only works with Google, and Google's charging for the upload to Colab? ...more like collaborator, amirite? dodges rotten eggs
The funny thing is the high egress fees Google charges for transferring data between two of its services (GCS -> Colab).
You can't just "buy servers for the same price". Where are you going to put them? How are you going to power them? How is the bandwidth for them provisioned? At some scale these are non-trivial questions - you can't just buy a rack, stick it next to your desk and plug into an extension cord.
The system deployment is something you need to spend time on as well. Bare metal provisioning and deployment of GPU libraries to make things run smoothly takes time.
And finally when the hype dies down in a week or two, what are you going to do with that infrastructure?
Cloud services are not trivial either. But they do have some advantages.
I agree with your post in general as intuition tell me (without further details about ops situation) that cloud is a competitive fit in this scenario.
Well, I guess that was the cost of getting it to go viral. The torrent seems quite active now.
There are a couple of things. First off, academic funding for things like AI-capable clusters is a whole thing, let alone the support staff for them. This is difficult for a host of reasons, some good and some bad.
Secondly, this isn't about hosting files really, it's about providing a suitable environment to run the model in. You can expect a AI research lab to have figured out how to do training in a reasonable way, but what they have here is more of an inference in production problem. That's really outside the expected expertise of a research lab. In the old days you just wouldn't be able to access this simply. Today there are turn-key(ish) solutions with built in scaling; they used one - it scaled and now they are wincing at the bill.
This isn't crazy, as the google->google egress fees are not obvious.
They overbudgeted the "infrastructure" section of their grants and have money to burn; and nobody's interested in setting up another datacenter.