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Cloud Run GPUs, now GA, makes running AI workloads easier for everyone

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41–50 of 191 posts

Re: Cloud Run GPUs, now GA, makes running AI workloads easier for everyone

#41

Im tired of using AI in cloud services. I want user friendly locally owned AI hardware. Right now nothing is consumer friendly. I can’t get a packaged deal of some locally running ChatGPT quality UI or voice command system in an all in one package. Like what Macs did for PCs I want the same for AI.

Hoping the DGX Spark will deliver on this

Re: Cloud Run GPUs, now GA, makes running AI workloads easier for everyone

#42

All the cruft of a big cloud provider, AND the joy of uncapped yolo billing that has the potential to drain your creditcard overnight. No thanks, I'll personally stick with Modal and vast.ai

You can set max instances in Cloud Run, which is an effective limit on how much you'll spend. Also, hard dollar caps are rarely if ever the right choice. App Engine used to have these, and the practical effect was that your website would completely stop working exactly when you least want it to (posted on HN etc). It's better to set billing alerts and make the call yourself if they go off.

> Also, hard dollar caps are rarely if ever the right choice.

Depends on if you're a big business or an individual. There is absolutely no reason I would ever pay $100k for a traffic burst on my personal site or side project (like the $100k Netlify case a few months ago).

> It's better to set billing alerts and make the call yourself if they go off.

Billing alerts are not instant and neither is anyone online 24x7 monitoring the alerts.

Re: Cloud Run GPUs, now GA, makes running AI workloads easier for everyone

#43
post #34
post #9

Earlier quoted context omitted.

Even regular Cloud Run can take a lot of time to boot (~3 to 30 seconds), so this can be a problem when scaling to 0

That's not my experience, using Go. Never measured, but it goes to 0 all the time, so I would definitely noticed more than a couple of seconds.

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Re: Cloud Run GPUs, now GA, makes running AI workloads easier for everyone

#44
post #37

> Time-to-First-Token of approximately 19 seconds for a gemma3:4b model (this includes startup time, model loading time, and running the inference) This is my biggest pet-peeve with serverless GPU. 19 seconds is a horrible latency from the user’s perspective and that’s a best case scenario. If this is the best one of the most experienced teams in the world can do, with a small 4B model, then it feels like serverless…

Looks like GPU instances not "lambda", so presumable you would over-provision to compensate.

Re: Cloud Run GPUs, now GA, makes running AI workloads easier for everyone

#45

All the cruft of a big cloud provider, AND the joy of uncapped yolo billing that has the potential to drain your creditcard overnight. No thanks, I'll personally stick with Modal and vast.ai

Runpod is pretty great. I wrote some genetic end point script that I can deploy in seconds, download the models to the pod, and Im ready to go. Plus I forgot and left a pod running, but down, for a week and it was like 0.60, and they emailed me like 3 times reminding me of the pod.

Re: Cloud Run GPUs, now GA, makes running AI workloads easier for everyone

#47

All the cruft of a big cloud provider, AND the joy of uncapped yolo billing that has the potential to drain your creditcard overnight. No thanks, I'll personally stick with Modal and vast.ai

Not providing a cap on spending is a major flaw of GCP for individuals / small projects. With Cloud Run, AFAIK, spending can effectively be capped by: limiting concurrency, plus limiting the max number of instances it can scale to. (But this is not as good as GCP having a proper cap.)

Amazon is the same I think? I live in constant fear we will have a runaway job one day. I get daily emails to myself (as a manager) and to my finance person. We had one instance where a team member forgot to turn off a machine for a few months :(

I get why it is a business strategy to not have limits .. but I wonder if providers would get more usage if people had more trusts on costs/predictability.

Re: Cloud Run GPUs, now GA, makes running AI workloads easier for everyone

#48
post #42

Earlier quoted context omitted.

You can set max instances in Cloud Run, which is an effective limit on how much you'll spend. Also, hard dollar caps are rarely if ever the right choice. App Engine used to have these, and the practical effect was that your website would completely stop working exactly when you least want it to (posted on HN etc). It's better to set billing alerts and make the call yourself if they go off.

> Also, hard dollar caps are rarely if ever the right choice. Depends on if you're a big business or an individual. There is absolutely no reason I would ever pay $100k for a traffic burst on my personal site or side project (like the $100k Netlify case a few months ago). > It's better to set billing alerts and make the call yourself if they go off. Billing alerts are not instant and neither is anyone online 24x7 mon…

100% agreed. This can be solved with technology .. let users set a soft and hard threshold for example. Runaway costs is the problem here.

Re: Cloud Run GPUs, now GA, makes running AI workloads easier for everyone

#49
post #46

A small and independent EU GPU cloud provider, DataCrunch (I am not affiliated), offers VMs with Nvidia GPUs even cheaper than Run Pod, etc 1x A100 80Gb 1.37€/hour 1x H100 80Gb 2.19€/hour

Or go P2P with Vast.ai, cheapest A100 right now is a setup with 2x A100 for $0.8/hour (so $0.4 per A100). Not affiliated with them, but mostly happy user. Be vary of network speeds though, some hosts are clearly on shared bandwidth and reported numbers don't always line up with reality, which kind of sucks when you're trying to shuffle around 100GB of data.
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