We're in the middle of an unprecedented GPU shortage. The numbers tell a stark story: demand for AI compute grew 400% in 2024, while supply grew just 40%. The result? H100s commanding $40,000+ with 52-week lead times. Every organization running AI workloads is fighting for the same limited compute.
But here's what most analyses miss: the shortage isn't just about getting GPUs—it's about using them well once you have them. And that's where visibility becomes critical.
The Numbers Behind the Shortage
These numbers represent a fundamental shift in how we need to think about GPU infrastructure. When GPUs were plentiful and cheap, inefficiency was tolerable. A job waiting an extra hour? An underutilized cluster overnight? Not ideal, but not catastrophic either.
Today, every hour of GPU time is precious. Every wasted cycle has a direct cost—not just in dollars, but in delayed experiments, missed deadlines, and competitive disadvantage.
The Visibility Gap in GPU Infrastructure
Modern GPU infrastructure is remarkably sophisticated. We have powerful schedulers like Kubernetes and Slurm. We have monitoring stacks—Prometheus, Grafana, the works. We can see GPU utilization, memory usage, queue lengths.
But ask the simplest question—"When will my job actually start?"—and most systems go silent.
We can tell you everything about what's happening now. We can't tell you anything about what happens next.
— Platform Engineer at a Top-5 AI Lab
This visibility gap has real consequences. Without predictability, teams develop coping mechanisms that make everything worse:
Over-requesting resources
Teams pad their GPU requests 'just in case,' reducing effective capacity for everyone.
Poor timing
Jobs get submitted at peak hours because nobody knows when the quiet times are.
Constant context-switching
Engineers refresh status pages instead of doing actual work.
Guesswork capacity planning
Leadership makes GPU purchasing decisions based on feelings, not data.
The True Cost of Poor Visibility
Let's do some back-of-envelope math. Consider a mid-size GPU cluster:
Cost Impact Model
And that's just the direct compute cost. Add in engineer productivity—hours spent waiting, checking status, and context-switching—and the true cost multiplies. For larger organizations running thousands of GPUs, we're talking millions in annual waste.
The Hidden Multiplier
What Changes With Visibility
The solution isn't more GPUs—at least, not primarily. The solution is visibility: giving teams the information they need to make good decisions.
Engineers plan their day
When you know a job will start in 3 hours, you can do productive work in the meantime instead of constantly checking.
Teams optimize naturally
With visibility into queue patterns, teams shift submissions to off-peak times without being told to.
Capacity decisions improve
Leadership can see actual demand patterns and make informed purchasing decisions.
Culture gets healthier
No more blame games. No more 'why did their job run first?' When everyone can see what's happening, trust improves.
The Path Forward
The GPU shortage isn't going away soon. If anything, as AI becomes more central to business strategy, demand will continue to outpace supply. The organizations that thrive won't necessarily be those with the most GPUs—they'll be those that use their GPUs most effectively.
Visibility is the foundation of that effectiveness. It's not glamorous. It won't make headlines like a new model architecture. But it's the difference between a well-run infrastructure and one that's constantly fighting fires.
In a world of GPU scarcity, the competitive advantage goes to teams that can do more with less. That starts with knowing what you have and when you can use it.
This is the problem we're solving at VGAC
We're building visibility into GPU queue scheduling—so teams know when jobs will run before they submit, and can plan accordingly.
Learn more about VGAC