GPU Cloud Deployment Without Leaving Your IDE — Audry Hsu, RunPod

Hey everyone.

I'm Audry. I work at RunPod. Were any of you in my earlier session? OK, good. Because then I'm going to. The intro is the same, but what I'm going to show is a little bit different. Has anyone heard of RunPod or used RunPod before?

You have? Do you mind if I ask you how you've used us or heard about us before?

At other universities we have some RunPod credits, so I use this for LLM training.

LLM training? OK. At your university? And where do you go to uni?

UOXford.

Oxford? I did a study abroad there one summer. It's awesome there. OK. Love it. OK. And your name is.

Yunus.

Yunus. OK. So Yunus might know a little bit about this already, but I'll talk you guys through a little intro. What do we do? We're an AI cloud infrastructure company, and our mission is to build the foundational platform for developers to scale their AI workloads.

And basically what that means is we bring the hardware, we bring the GPUs and the compute, we make it easy for you guys to bring your code, bring your models, and deploy as quickly as possible. We don't want you spending time configuring infrastructure and thinking about things like scaling.

Why RunPod exists? A lot of teams that we've talked to, they are all wrestling with the same thing, that infrastructure they're spending more time with the infrastructure than they are with the models. So things like CUDA version alignment, like what versions of PyTorch run well together,

which new of the GPU SKUs have been tested, and figuring out the bugs there. So a lot of that are things that we try to take that configuration problem away from you guys so you guys can just focus on training your model or building your apps.

And a little bit of a backstory about our company. So this is Zen and Pradeep, our two founders. They started RunPod in 2022. They had a failed crypto mining venture, so they had a bunch of spare GPUs in their basement.

They built a prototype of what is the foundation of RunPod today, and they just posted on Reddit and said, does anyone want some free GPUs in exchange for feedback? And that is literally how our company started, and ever since then we've been building in public with the community.

So we've been revenue generating from the very beginning, which is very, very rare. And even today we have around 500 developers on our platform. We're in 30 plus data centers across 10 countries. In Europe that includes France, Romania, Iceland, if that's part of Europe, Asia Pacific.

And we recently hit a pretty big milestone of $120 million in annual recurring revenue. This is just a quick glance of some of the customers that we have. You might be surprised seeing that some of these are AI native companies and some large enterprises as well.

And kind of the bottom line of what they have in common is that they need flexible and reliable GPU infrastructure. So I definitely would say we're punching above our weight class.

Really quick high level, there's different ways to build on RunPod depending on what you're trying to do. So if you need a more persistent VM environment, then pods is a great use case. If you can rent a pod on demand, pay by the second, and once you're done you can tear it all down and start again.

Pods are if you need reserved GPU. So as long as your pod is running, then the GPU is yours and no one can take it away from you. Serverless, if you have if you're ready to deploy something and you care more about scaling, so your workloads are more variable in terms of frequency and load, we help you auto scale your workers for you and scale them back down when you don't have any requests happening so you don't pay for any idle time.

Clusters, great use case for training, multi-node. And then hub is also a place where you can deploy already open source AI repos that have already been pre-vetted by us for popular models like ComfyUI, Stable Diffusion, VLLM. And that's one way if you're just exploring to just click around and get started really quickly.

I'm going to talk about serverless today and the product that we just and I'm going to switch my displays again here really quick so we can mirror my screen.

So one of the things that is a huge pain for developers is if they're still in the iteration or the development phase. So normally

when you are working on, let's say, some code around your inference model and you're still testing things out, you have to make a commit, push it to GitHub, build your Docker image, pull it down from the container registry, and then load it onto a server, and then allocate a GPU on it, and then you get to test it and see if it's working as you expect it.

And then you do that all over again until you're ready. So the problem that Flash is trying to solve here, and Flash is our Python SDK, is that we want to eliminate all of that iteration cycle so that you can deploy your function on a GPUright from your local development environment.

And I'll zoom in here really quick. So this is all you need to know about Flash in one little paragraph is you have a regular async Python function, you add our Flash endpoint decorator, and it's going to deploy and package everything inside your function onto a GPU cloud.

Everything around it, your main function, any helper functions that you have, those all run on your local development environment. But if you need GPU compute, that can run on the cloud. And you can we have file reload, so if you change anything in your application anywhere, then it gets repackaged and pushed up immediately, and you can test and iterate super quickly.

And I'm just going to show an example of this.

OK.

So I have a function here, generate image. Simply I'm loading PyTorch, I'm loading a pre-trained Stable Diffusion model, Stable Diffusion XL Turbo, really great for fast generation of images. And I'm going to save the image down, and that's going to return it base64 encoded.

So I can run thisright now here. I've already installed all my dependencies, I already have a Flash project going. I'm going to Flash run

image generation py. And what I'm going to actually do is I have a little

Flash

run. So Flash run spins up a local development server here. It's just a fast API server. And I can send my request here

to this endpoint. And I'm going to do that really quickly, just get to my project. And

this is just a little helper script that's going to send a POST request to it and then decode that image so that you guys actually get to see what it looks like once it's generated.

And it was

image generation

async. There we go.

And let's pass a prompt to it. Can I get a help from the audience? What do we want to generate today? Literally anything. Anything random.

Cats flying in the sky.

OK. Cats flying in the sky. What does the sky look like? What time of day is it?

Cloudy.

London.

In London.

Flying in flying on a cloudy day in the sky somewhere in London. And I passed it correctly.

He's looking at it so closely. He's helping me debug live. I love it. Thank you.

It's a bit hard to see in dark mode.

I passed URL, and that's true.

Place after localhost.

I must have.

Oh, you've got oh, never mind.

Is it my HTTP?

OK, there we go.

OK. Going back to the local dev server. It sees the request, it started the job, it's queued it.

And we're just going to wait for a second to see if it finishes. And so while that's happening, let me bring your attention back to

make it bigger for you guys.

The endpoint decorator. So this is where all the magic happens.

I have passed a name for my endpoint. I specify a GPU family. So the ADA 80 Pros, these are different variations of NVIDIA H100 cards. I can specify my max number of workers to be five, so I can have at max five of them running at once.

I just put one active worker, so this is one that's always going to be running and always on. And that's definitely a dragon.

And it didn't take my prompt, probably because I did I not pass it as a I didn't pass it as a flag.

Prompt. There we go. OK, now it's definitely generating cats flying.

OK, back to the endpoint decorator. And then there's other different configurations for timeout, which is how long a worker is idle. Here we go.

OK, this looks terrible, guys.

They are cats. They're abstract cats. And I'm not from London, but maybe someone can tell me if this looks like a London Jiminy? Maybe. OK, so I don't like I don't like what just happened. So what we're going to do instead is we're going to switch out our model.

So I'm just going to comment out this code here. And then down here,

let's swap in DreamShaper, which is a fine-tuned model based off of Stable Diffusion 1.5. So this one is while where Stable Diffusion XL Pro is more optimized for just quick generation, I think this one is going to generate a better quality image for us.

And it's specifically better for kind of like more art and Illustrator styles. So we've changed some of the parameters in it. It's going to have a few more inference steps to it. I'm just going to set that to 25.

Height and width, 10 by 24. That's fine. And let's just send the same request again.

And let's see what happens. What's different?

So again, what made this really fast is instead of making a code change, committing it, rebuilding my Docker, uploading it somewhere, and then allocating GPU infrastructure, all of this is happeningright here from my IDE, and I never have to leave.

This is good,right, guys? We like this one? OK, so one more one last thing that I'm just going to show you guys to round things out is I think where using a developer tool like Flash makes a big difference is when you're trying to not just like make one single call to one model.

It's all about all the orchestration code around it,right? So I have here a pipeline that I've pre-prepared. And what it's going to do is instead of me generating and writing out every prompt, it's going to send a request to Qwen that's already hosted on a public endpoint.

And Qwen 3 is going to generate all the prompts for me. And then after that, it's going to send that to our DreamShaper running on our endpoint. And then after that, there's one more pipeline that it goes through.

It's going to send the request to Nano Banana 2, which is a premium Google model that's really good at composing photos together. And I'm hoping that I can compose some cool pictures of our founders, and I can send them to them after this demo is done.

OK.

Now let's run the whole pipeline here and check. OK, prompt

two men walking in London on a it is cloudy today. Cloudy today. Close-up of their faces.

Any other requests for these two men?

AI engineers? AI engineers? I don't know what you see.

How do they look? Like, are they doing something?

Glasses?

Glasses, yeah. OK. Two men

with glasses. Close-up of their faces.

OK, and let's generate

let's generate three of those, and let's compose it together.

OK.

So how does it work in terms of pricing?

Pricing? Sure. So every request that we send to it, you're only charged for how long that request is running. So let's see. I'm going to I said this whole session was going to be only in the terminal, but I'm going to go back into the console just to show you what's running.

Let's see.

This is the endpoint that we created from the terminal. Here are the workers. I think we said like five workers, so we have about five or six here that are provisioned. Three of them are running because I asked for three photos.

And so this is uptime. This is what you're being charged for. And let me see. The cost of an H100right now is 0.00116 cents per second.

So is it the same as for a pod, or is it is the pricing a bit different?

Pricing is a little bit different for serverless versus pods because pods, you don't get any of the scaling with it. So there is a little bit of a premium for serverless. So what we usually recommend is if you're still experimenting, then either start with a very low worker count or start with pods,right?

Because when you're experimenting, you might only need a limited number of GPUs, one GPU at a time, two GPUs at a time. Serverless, for when you need hundreds of workers running on hundreds of GPUs, and you want them distributed for better availability across different data centers.

OK, guys, here's our final presentation. So this was our original prompt. Two men with glasses walking in London on a cloudy day. Close-up of their faces. So on the left, this is what DreamShaper generated based off of the prompt engineering that Qwen 3 did for us.

So it's a lot better of a prompt than what I sent in. It has a lot better cues about notes on, like I can read it out to you since I know it's hard to see. Thoughtful expressions and weathered faces, soft focus on background clouds, muted urban palette with grays and deep blues over cast lighting.

And then on theright is the final composed photo of this is a very handsome picture of Pradeep, and this is somehow a very old photo of Zen. And I'm just going to scroll down to show you that this is the reference photo that I sent in it.

But overall

overall, yeah, I just wanted to show you guys, like, this is how you can get started really quickly. You can start in your local development environment. You can use open-sourced models. You can bring your own model, private model.

And this was really fun to do. So thank you, guys, for hanging out with me.