Visibility problems in GPU clusters rarely announce themselves. They show up as symptoms— frustration, inefficiency, conflict—that get attributed to other causes. "We just need more GPUs." "The scheduler is bad." "People need to be more patient."
Sometimes those explanations are right. But often, the root cause is simpler: people don't have the information they need to work effectively.
Here are five signs that your cluster might have a visibility problem—and what you can do about it.
"When Will It Run?" Is Your Most Common Question
Walk through your Slack channels or stand near the coffee machine. How often do you hear some variation of "when will my job start?" If it's multiple times a day, you have a visibility problem.
What this looks like:
- •Engineers DMing platform teams for status updates
- •Dedicated Slack channels for queue status questions
- •Standup meetings derailed by queue discussions
- •Platform team spending hours on "when" questions instead of improvements
The Real Issue
Engineers Work Nights and Weekends
Not because of deadlines—because of queues. When engineers discover that jobs submitted at 2 AM run faster, they start working at 2 AM. This isn't dedication; it's a symptom of broken visibility.
The Problem
Engineers game the system because they can't see it. They learn patterns through painful trial and error.
With Visibility
Teams would know the best times to submit, plan accordingly during work hours, and maintain healthy work-life balance.
Resource Requests Don't Match Usage
Look at your cluster's resource requests versus actual utilization. If there's a significant gap—jobs requesting 8 GPUs but only using 4, or requesting 24 hours but finishing in 6—you likely have a visibility-driven over-requesting problem.
Why this happens:
When queue times are unpredictable, people pad their requests. "I might need 8 GPUs, and I might need 24 hours. If I request less and have to requeue, I'll lose my spot." This is rational behavior given poor visibility—but it creates a tragedy of the commons where everyone's padding hurts everyone's queue times.
Duplicate Jobs Clog the Queue
When people don't know if their job will run "soon" or "eventually," some submit it multiple ways: with different resource configurations, to different partitions, or simply multiple copies hoping one gets through faster.
I submitted it three ways because I didn't know which would run first. I know it's bad, but what else can I do?
— ML Engineer on a shared cluster
This creates a vicious cycle: duplicate jobs clog the queue, making wait times less predictable, which encourages more duplicate submissions.
Capacity Discussions Are Heated
"We need more GPUs" vs "We need to use our GPUs better" shouldn't be a religious war. But without visibility data, both sides are arguing from intuition.
Questions you should be able to answer:
- •What's our actual queue wait time distribution?
- •When are our peak usage periods?
- •How much of our "queue problem" is capacity vs scheduling inefficiency?
- •What would adding X GPUs actually do to wait times?
If you can't answer these questions with data, every capacity discussion becomes a political battle rather than an engineering decision.
What To Do About It
If you recognized your organization in these signs, the good news is that visibility problems are solvable. The first step is acknowledging that this is a problem worth solving—not just "how things are."
That's exactly what we're building at VGAC: visibility into GPU queue scheduling that answers the simple question everyone's asking—"when will my job run?"