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

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141–150 of 191 posts

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

#141
post #92

Earlier quoted context omitted.

I agree, but in the GCP world, a lot of these things are merging. My understanding is that Cloud Run, Cloud Run Functions (previously known as Cloud Functions Gen2) and even App Engine Flexible all run in the same underlying cloud run infrastructure, so it's essentially just some interface differences that to me now seem more like historical legacy/backwards compatibility reasons than meaningful functionality differe…

FWIW, App Engine Flexible is a different product that runs on GCE VM. Other products (App Engine standard, Cloud Functions gen1, Cloud Run, Cloud Run Functions) share many underlying infrastructures.

Oh, thanks! I guess I had it backwards - I thought App Engine standard was the one on a different infrastructure.

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

#143
post #51
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…

That has to be cold-start, and next N requests would surely be using the already started thing? It sounds bananas they'd even mention using something like that with 19 seconds latency for all requests in any context.

That's true. Traditional single-tier storage can not meet the throughput and latency demand. My cofounder wrote this piece on a three-tiered storage architecture optimized for both performance and cost - https://nilesh-agarwal.com/three-tier-storage-architecture-f...

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

#145
post #20

The pricing doesn't look that compelling, here are the hourly rate comparisons vs runpod.io vs vast.ai: 1x L4 24GB: google: $0.71; runpod.io: $0.43, spot: $0.22 4x L4 24GB: google: $4.00; runpod.io: $1.72, spot: $0.88 1x A100 80GB: google: $5.07; runpod.io: $1.64, spot: $0.82; vast.ai $0.880, spot: $0.501 1x H100 80GB: google: $11.06; runpod.io: $2.79, spot: $1.65; vast.ai $1.535, spot: $0.473 8x H200 141GB: google:…

> Google's pricing also assumes you're running it 24/7 for an entire month

What makes you think that?

Cloud Run [pricing page](https://cloud.google.com/run/pricing) explicitly says : "charge you only for the resources you use, rounded up to the nearest 100 millisecond"

Also, Cloud Run's [autoscalling](https://cloud.google.com/run/docs/about-instance-autoscaling) is in effect, scaling down idle instances after a maximum of 15 minutes.

(Cloud Run PM)

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

#146
post #54

I love Google Cloud Run and highly recommend it as the best option[1]. The Cloud Run GPU, however is not something I can recommend. It is not cost effective (instance based billing is expensive as opposed to request based billing), GPU choices are limited, and the general loading/unloading of model (gigabytes) from GPU memory makes it slow to be used as server less. Once you compare the numbers it is better to use a…

google vp here: we appreciate the feedback! i generally agree that if you have a strong understanding of your static capacity needs, pre-provisioning VMs is likely to be more cost efficient with today's pricing. cloud run GPUs are ideal for more bursty workloads -- maybe a new AI app that doesn't yet have PMF, where you really need that scale-to-zero + fast start for more sparse traffic patterns.

How does that compare to spinning up some ec2s with amazon trainium gpus?

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

#147
post #127
post #66

Earlier quoted context omitted.

Setting max instances effectively caps your spend right?

Yes. CR has had this feature since day 1, people just don't bother to read the docs and would rather write long blog posts blaming their cloud provider for manufacturing the gun they shot themself in the foot with.

[deleted]

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

#148

Earlier quoted context omitted.

Cloud Run is a great service. I find it much easier to work with than AWS's equivalent (ECS/Fargate.)

AWS AppRunner is the closest equivalent to Cloud Run. Its really not close though, AppRunner is an unloved service at AWS and is missing a lot of the features that make Cloud Run nice.

I agree with the unloved part. It was a great middle ground between Lambda and Fargate (zero cold start, reasonable pricing), but has seemingly been in maintenance mode for quite a while now. Really sad to see.

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

#149
post #127
post #66

Earlier quoted context omitted.

Setting max instances effectively caps your spend right?

Yes. CR has had this feature since day 1, people just don't bother to read the docs and would rather write long blog posts blaming their cloud provider for manufacturing the gun they shot themself in the foot with.

[deleted]

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

#150
post #54

I love Google Cloud Run and highly recommend it as the best option[1]. The Cloud Run GPU, however is not something I can recommend. It is not cost effective (instance based billing is expensive as opposed to request based billing), GPU choices are limited, and the general loading/unloading of model (gigabytes) from GPU memory makes it slow to be used as server less. Once you compare the numbers it is better to use a…

The problem is you can't reliably get VMs on GCP. All the major clouds are suffering from this. AWS you can't ever get an 80gb gpu without a long term reserve and even then it's wildly expensive. GCP you can sometimes but its also insanely expensive. These companies claim to be "startup friendly", they are anything but. All the neo-clouds somehow manage to do this well (runpod, nebius, lambda) but the big clouds are…

To massively increase the reliability to get GPUs, you can use something like SkyPilot (https://github.com/skypilot-org/skypilot) to fall back across regions, clouds, or GPU choices. E.g.,

$ sky launch --gpus H100

will fall back across GCP regions, AWS, your clusters, etc. There are options to say try either H100 or H200 or A100 or .

Essentially the way you deal with it is to increase the infra search space.

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