If you've ever struggled to get hold of a decent GPU, be it to train a model, render video, or build an AI startup, you may've felt the sting. You're not the only one. GPUs are scarce, expensive, and tantalizingly out of reach when you most desperately need them. And while you're racing toward a product launch, investor meeting, or production timeline, waiting in a queue for compute feels like sabotage.

We get it at Raadr. You're in a hurry to go fast, build something great, and not be hampered by infrastructure. So let's break what's really going on, and better still, how to remain one step ahead of the chaos.

What's the GPU crunch all about?
Everyone seems to want GPUs now. AI models continue to increase in size, video game titles are graphically intensive, and even cryptocurrency hovers in the corners. The demand is surging, and the manufacturers just aren't able to keep up. TSMC and Samsung are sold out, and the supply chains all over the globe haven't fully recovered from the past few years of disruptions.

Add to all this the information that the big players like Google, OpenAI, and Amazon are grabbing giant chunks of the available supply. This leaves the rest with less. And when GPU availability shows up in the cloud, the price tag is dizzyingly steep. Spot instances are snapped up nearly immediately. Reserved instances? Good luck affording them if you're a small company or startup.

All of this puts a lot of stress on your timeline. Model training gets delayed. Inference work grinds to a halt. Animations won't even render. Teams will start rushing the deadlines or burning funds just to keep the ball moving. Things get even worse when your engineers spend time devising GPU workarounds instead of getting the thing done they were hired to do.

So how do you get around the shortage?
First, be creative with the infrastructure. A hybrid setup with GPUs locally for the day-to-day dev operations and the cloud for the big stuff will give you the best bang for the buck and reduce reliance on the cloud. And don't rule out alternatives. The smaller GPU players like CoreWeave, Vast.ai, and RunPod and others will typically have better availability and pricing than the big players.

Workload optimization is a major benefit. Techniques involving model distillation, model quantization, even the choice of using FP16 or INT8 precision, can save hours of time to train. It's a question of completing the task faster, not a question of dollars.

Smart scheduling software can be a game-changer as well. With technology such as Kubernetes (and the NVIDIA GPU Operator), Ray, or SkyPilot, scaling automation, maximum utilisation, and handling spot instance variations won't keep you up all night.

There are now vendors who enable pre-booking of GPU time. Think of it the same as booking a meeting room, but for your AI work. Reserve, complete the work, done.

And lastly, stay plugged in. Join the Discords, watch the forums, turn on the notifications. Occasionally "GPU unavailable" vs. "GPU online" becomes a matter of the first click.

This Is What We're Solving at Raadr
We built Raadr because we were tired of spending too much time hunting for GPUs instead of using them. Our platform enables you to find, compare, and pre-book GPU time on different cloud providers without sitting in front of ten dashboards and refreshing all day. We do the drudgery for you to make it easier, allowing you to build. GPU shortages won't ruin your timelines. And with the right equipment, they won't. Ready to skip the waiting game and ship immediately? Join the waitlist at Raadr and take control of your compute.