Your team submits a GPU job and has no idea when it'll run. VGAC tells you why jobs are stuck, when they'll start, and how to get them running faster — so you stop refreshing status pages and start shipping.
You're running a world-class ML team on a cluster you can't predict. Every job submission is a leap of faith.
Your team submits jobs and has no idea when they'll run. Productivity is lost to guessing, checking, and waiting.
Jobs submitted at the wrong time. Poor utilization patterns. You're paying for compute that isn't being used efficiently.
Engineers wait instead of iterate. Experiments get delayed. Deadlines slip because nobody can plan around queue times.
No visibility into cluster patterns. Can't anticipate bottlenecks. Every capacity decision is based on gut feeling.
Sound familiar? There's a better way.
VGAC learns your cluster's behavior and tells your team exactly when their jobs will run. No more guessing. No more wasted time. Just reliable predictions you can plan around.
No complex setup. No workflow changes. Just connect and start getting predictions.
Point VGAC at your Slurm, Kubernetes, or PBS scheduler. It starts collecting GPU metrics, job events, and queue state automatically. No code changes required.
Slurm · K8s · PBSBefore you submit, see how long your job will wait. VGAC learns your cluster's patterns — which partitions are busy, when the quiet hours are, which job sizes move fastest.
Pre-submit predictionsVGAC spots scheduling problems before they cascade. Peak-hour contention building up? Memory pressure on a node? You'll know before the queue backs up — not after.
Predictive alertsSee right-sizing suggestions, alternative placements, and auto-generated Slurm scripts. Platform teams get capacity forecasts and utilization insights to make data-driven decisions.
Actionable insightsYour researchers shouldn't need to ask Slack when their job will run. VGAC gives them the answer.
See expected wait times before your job enters the queue. VGAC tells you if now is a good time to submit, or if you should wait an hour and skip a 3-hour queue.
Not just 'your job is pending.' VGAC explains the bottleneck: is it queued behind large jobs? Is the partition at capacity? Are other users holding GPUs they're not using?
Requesting 8 GPUs when you only need 4 doubles your wait time and blocks everyone else. VGAC analyzes your job and suggests the fastest path to getting it running.
VGAC detects scheduling patterns — like peak-hour contention or cascading delays — and warns you before the queue backs up. Stop firefighting, start planning.
Curious what this looks like in practice? Let's talk.
Whether you're a startup or enterprise, research lab or cloud provider — if you run GPUs, VGAC helps.
Fortune 500 & Large Tech
Your GPU cluster runs 24/7. Dozens of teams submit jobs constantly. Without visibility, it's chaos. VGAC gives every team member predictable scheduling, so they can plan their work and hit deadlines.
"We went from constant Slack messages asking 'when will my job run?' to everyone just knowing."
— ML Platform Lead
Perspectives on GPU infrastructure challenges and the future of ML operations.
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We've lived this problem—running GPU clusters, waiting on queues, and wishing we had visibility. Now we're building the solution.
Founder & Lead Engineer
Platform engineer with 8+ years building cloud-native systems at scale. SRE at Sportserve, Research Software Engineer at EcoHealth Alliance (GPU clusters for ML workloads), and founding engineer at Kustode. Deep expertise in GPU resource management, Kubernetes scheduling, and observability systems.
Interested in joining the team? Let's talk
GPU compute is exploding, and teams need better visibility into their infrastructure. We're building a product to solve a real, widespread problem.
GPU infrastructure is one of the fastest-growing markets in tech. Every organization running AI workloads needs better visibility.
Queue uncertainty is a universal pain point. Teams we talk to immediately recognize the problem and want a solution.
Our team has spent years studying GPU cluster behavior. We're applying that expertise to a real-world product.
We're sharing our journey and learning from the community. The teams we talk to consistently recognize this problem.
We're raising our seed round and would love to share more about what we're building and where we're headed.
VGAC is open source. Explore the codebase, run it locally, or deploy to your cluster. Calibrated predictions from day one.
No spam. We'll reach out to schedule a demo.