Train on hardware you own. Know who used every GPU hour.
Vantage Compute runs training and fine-tuning jobs on your own clusters and cloud accounts, under one identity across Slurm and Kubernetes, with usage attributed to each team.
GPU clusters built for one team, shared by ten.
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Teams compete for the same GPUs with no fair share and no chargeback.
- 02
Nobody can show leadership which team used which GPUs, or what it cost.
- 03
Each cloud and on-prem cluster carries its own scheduler, accounts and access model.
One control plane for every training run.
Engineers sign in once with your identity provider and reach every Slurm and Kubernetes cluster they are allowed to use.
Fine-tuning and pre-training share capacity by policy, with GPU hours and spend tracked per team and project.
Submit the same job definition from the UI, CLI or API, on any connected cluster.
Share GPUs fairly, and prove who used them.
From submission to result.
Any connected cloud or on-prem cluster, Slurm or Kubernetes.
The same job definition works on every surface.
Quotas and access rules apply before the job starts.
GPU hours land on the team and project that used them.
- Foundation Model Trainers
- ML Platform Engineers
- Fine-Tuning Engineers
- Distributed Systems Engineers
- Training Infrastructure SREs