--- title: Vantage Compute | Secure Control Plane for Sovereign AI and HPC canonical: 'https://www.vantagecompute.ai/' description: >- Hardware is the easy part. Vantage Compute secures the layer on top: one identity across Slurm, Kubernetes and cloud, policy enforced, and a record of who used what. --- Sovereign AI & HPC · Secure Control Plane # Hardware is the easy part. We secure the layer on top. Vantage Compute gives every user one identity across Slurm, Kubernetes and cloud, enforces policy before a job runs, and records who used what. Built for\ Research computing leaders. [Book Demo](https://www.vantagecompute.ai/contact/) [Log In →](https://app.vantagecompute.ai/) vantage · zsh ![Vantage cluster list spanning AWS, Google Cloud, Azure, and on-prem](https://www.vantagecompute.ai/_opt/assets/vantage-ui-clusters-1600.png) Clusters across every cloud and your own racks Every distributed training run in one view Slurm and Kubernetes schedulers, side by side Notebooks on GPU clusters, in seconds ![NVIDIA NIM and Hugging Face model catalog in Vantage](https://www.vantagecompute.ai/_opt/assets/vantage-ui-catalog-1600.png) Open models, pinned and served on your clusters Runs everywhere you do ![Amazon Web Services](https://www.vantagecompute.ai/assets/aws-logo.png) ![Microsoft Azure](https://www.vantagecompute.ai/assets/azure-logo.png) ![Google Cloud](https://www.vantagecompute.ai/assets/google-cloud-logo.png) ![Oracle Cloud Infrastructure](https://www.vantagecompute.ai/assets/oracle-logo.png) The Vantage Compute Control Plane ## One control plane. Inside your boundary. Run AI and simulation on hardware you own, and use cloud only when you choose. ![Vantage Compute architecture: one control plane over Slurm, Kubernetes and every cloud](https://www.vantagecompute.ai/_opt/assets/architecture-infographic-1600.png) Own the whole stack. Run frontier AI on your terms, in your jurisdiction Hold five controls the platform enforces, rather than promises in a contract. ### One Control Plane for Slurm and Kubernetes **Bring Slurm to Kubernetes** Run your existing HPC workloads without refactoring or compromise **Automated Cluster Provisioning** Stop running two worlds with two identity systems. Provision Slurm, Kubernetes, or both from one place ![Isometric cubes illustrating cluster orchestration](https://www.vantagecompute.ai/assets/k8s-orchestration-graphic.svg) ![Key and layers illustrating identity-aware access](https://www.vantagecompute.ai/assets/identity-access-graphic.svg) ### Sovereign by Design **One Identity, One Audit Trail** Every user carries one identity across Slurm, Kubernetes, and AI tools, and every action is logged against it **IAM Simplified** Identity-based authentication eliminates passwords across compute, storage, and workloads ### Use What You Already Own **Stay Secure, Stay Running** Active security monitoring and safe update management protect your GPU workloads **Recover Idle GPUs** Find idle capacity, share GPUs fairly through partitioning, and show leadership what each team uses. ![Isometric GPU chip illustration](https://www.vantagecompute.ai/assets/gpu-operations-graphic.svg) [Book Demo](https://www.vantagecompute.ai/contact/) ## Vantage Compute gives every user one identity across Slurm, Kubernetes and cloud, on hardware you own. Sovereign & Open ## Stop sending your research out to be hosted. Sovereign means your models, your hardware, and your jurisdiction, with data that never leaves your boundary. Run the open stack on infrastructure you own, and keep sensitive AI and simulation work in-house. ### Deploy inside your own boundary Install the full control plane in your own account or your own datacenter. Set the boundary once, and keep data and weights inside it. - On-prem - Your cloud account ### Serve open models, first class Pull open weights straight from NGC or Hugging Face, pin them to an immutable digest, and serve them on your own clusters, with no per-token vendor in the path. - NVIDIA NIM - Hugging Face - Open weights ### Keep the open stack underneath Migrate onto no proprietary scheduler. Keep the schedulers and runtimes your teams already run, and get the fixes back upstream. - Slurm - Kubernetes - Ray - Jupyter **Slurm and Kubernetes** One control plane, one identity model, no refactoring. ![Slurm Workload Manager](https://www.vantagecompute.ai/assets/logo-slurm-mark.png) ![Kubernetes](https://www.vantagecompute.ai/assets/logo-kubernetes-icon.svg) **Choose your cloud** AWS, Azure, and Google Cloud, or your own datacenter. ![Amazon Web Services](https://www.vantagecompute.ai/assets/logo-aws.svg) ![Microsoft Azure](https://www.vantagecompute.ai/assets/logo-azure.svg) ![Google Cloud](https://www.vantagecompute.ai/assets/logo-google-cloud.svg) **UI, CLI, API** Every action available to a person or a pipeline. ![Vantage](https://www.vantagecompute.ai/assets/vantage-logo-dark.svg) Sovereign AI · Governance & Security ## Draw the boundary. Watch it hold, and prove it held. Nominate the perimeter and keep the control plane, agents, schedulers, data, and models inside it. Authenticate every hop, authorize every request against your identity provider, and record every action. Sovereign boundary: your jurisdiction SCHEDULERS & WORKLOADS ![Slurm Workload Manager](https://www.vantagecompute.ai/assets/slurm-mark.svg) ![Kubernetes](https://www.vantagecompute.ai/assets/logo-kubernetes.svg) Apply one policy above both schedulers DATA & WEIGHTS Scale R\&D securely Grant mounts by policy, not by ticket ![Vantage](https://www.vantagecompute.ai/assets/vantage-logo-white.svg) Vantage control plane Run it in your cloud account or your own datacenter, reached by an outbound-only tunnel that opens no inbound ports UI CLI API SAME BOUNDARY, ANY SUBSTRATE ![Amazon Web Services](https://www.vantagecompute.ai/assets/logo-aws.svg) ![Microsoft Azure](https://www.vantagecompute.ai/assets/logo-azure.svg) ![Google Cloud](https://www.vantagecompute.ai/assets/logo-google-cloud.svg) + your racks Encrypt every hop Hold strict mTLS between every service in the mesh, and s2n TLS for the Slurm daemons, anchored to one private CA inside your cluster. Federate your identity Issue OIDC tokens from your own IdP and validate them at the gateway on every request. Resolve roles per API route, per workload, per namespace. Deny by default Grant access by explicit policy only: which regions, which images and models, which data, how much spend. Rotate and record Issue and rotate certificates automatically, and attribute every provision, submission, and access event to a person. Governance log live mtls workload cert issued to notebook pod, rotated automatically identity token validated at gateway, role **researcher** on project genomics tunnel outbound session established, no inbound port opened model digest sha256:9f2c… pinned from approved registry denied mount /phi refused, outside role's data scope quota team simulation at 82% of monthly allocation Answer the audit with a query, not a quarter-long project. Stream the log to your own SIEM, or hand an auditor a scoped view. [Talk to a solutions architect](https://www.vantagecompute.ai/contact/) ![NVIDIA Inception Program member badge](https://www.vantagecompute.ai/assets/nvidia-inception-program-badge-rgb-for-screen.png) ## Powered by NVIDIA and Open Source Vantage Compute is an NVIDIA Inception program member, built on open-source Slurm and Kubernetes. Point and click, script it, or wire it into your stack: same control plane underneath. AI Research training & inference Simulation Eng multi-node CFD & FEA HPC Ops queues, quotas, spend Platform & Business network · cluster eng · procurement · PM ![Vantage](https://www.vantagecompute.ai/assets/vantage-logo-white.svg) Security, Access, Control UI CLI API Slurm ![AWS](https://www.vantagecompute.ai/assets/aws-logo-color.png) gpu-prod · AWS 512 × H200 Kubernetes ![Google Cloud](https://www.vantagecompute.ai/assets/google-cloud-logo-color.png) inference · Google Cloud autoscale 0 → 128 Slurm Kubernetes ![Azure](https://www.vantagecompute.ai/assets/azure-logo-color.png) hybrid · Azure federated scheduling Slurm on-prem · datacenter burst to cloud illustrative example · every team · every cluster Provision clusters, manage schedulers, and track spend from one dashboard. Cluster names and sizes above are illustrative. Operational Intelligence ## Complete Cluster Control Via UI, CLI, or API. ![Clusters across AWS, Google Cloud, Azure, LXD, and on-prem in Vantage](https://www.vantagecompute.ai/_opt/assets/vantage-ui-clusters-1600.png) 01 · Provision ### Clusters anywhere, in minutes Stand up Slurm, Kubernetes, or both on AWS, Google Cloud, Azure, or your own racks, fully automated, no refactoring. Launch JupyterHub, Ray, Kubeflow, or Spark on top in seconds, and pull any NVIDIA NIM or Hugging Face model straight onto your own clusters. ![Distributed training jobs across every cluster in Vantage](https://www.vantagecompute.ai/_opt/assets/vantage-ui-training-1600.png) 02 · Operate ### Every job and every queue, one view Every distributed run in one place: NeMo, Kubeflow Trainer, Slurm batch, and sweeps, with GPU counts, runtimes, and queue position across clouds. Spot queue hotspots and node health issues fast, then pack jobs tighter to run more models on the same silicon. ![VCU spend breakdown by team in Vantage](https://www.vantagecompute.ai/_opt/assets/vantage-ui-vcu-matrix-1600.png) 03 · Govern ### Spend and access under policy Prove every GPU hour: attribute spend to specific teams and users, enforce quotas, and audit usage in real time. Manage policy at the workload level and integrate your IdP for granular, audit-ready permissions. [Book Demo](https://www.vantagecompute.ai/contact/) One Platform ## Built For AI Researchers One hundred job titles, one secure control plane: notebooks, batch jobs, distributed training, and large-scale simulation on hardware you own. AI Researchers @Life Sciences Launch experiments without filing an IT ticket. ML Platform Engineers @AI Labs One control plane for training, tuning, and serving across clouds. MLOps Engineers @FinTech Promote models from notebook to endpoint on the same policy. Foundation Model Trainers @AI Labs Built for multi-node training runs, each one tracked per team. Fine-Tuning Engineers @Enterprise AI Adapter runs queued per team, and billed per team. Inference Platform Leads @Enterprise AI Serve open models on your own clusters, pinned to a digest. Prompt Engineers @SaaS Batch evaluation sweeps without waiting on a shared notebook. RAG Pipeline Engineers @Legal Tech Embed, index, and re-rank on the same cluster as the data. Computer Vision Engineers @Robotics Training sets mounted by policy, beside the GPUs that use them. NLP Engineers @Customer Support Run tokenizer sweeps and evaluation harnesses side by side. Speech Recognition Engineers @Telecom Distributed training on audio that never leaves the region. Recommender Systems Engineers @Retail Nightly retrains queued by policy and tracked per team. Reinforcement Learning Researchers @Robotics Parallel rollouts under one scheduler and one identity. Model Evaluation Leads @AI Safety Reproducible evaluation runs with the environment pinned. AI Infrastructure Architects @Enterprise AI Design it once, then run it on Slurm and Kubernetes both. Applied Scientists @E-Commerce Move from prototype to production without changing stacks. Data Scientists @Insurance Notebooks backed by real GPUs instead of a laptop. Deep Learning Engineers @Medical Imaging Multi-node training on patient data that stays in your VPC. Generative Media Engineers @Entertainment Render passes and diffusion jobs on the same queue. Autonomy Research Leads @Automotive Every scenario replay tracked, run by run. Edge AI Engineers @Industrial IoT Train centrally on hardware you own, with every model version tracked. AI Product Engineers @Startups Serve open models on the clusters you already run. LLM Ops Engineers @Enterprise AI Every endpoint tied to a named owner and a pinned version. Synthetic Data Engineers @Autonomous Systems Generate corpora at cluster scale, not laptop scale. Vector Search Engineers @Search Index rebuilds scheduled beside live serving, by policy. Feature Platform Engineers @FinTech Batch and online jobs under one identity and one policy. Model Risk Analysts @Banking Every run logged and tied to the person who launched it. AI Governance Leads @Public Sector Access and residency rules enforced before the job starts. Research Engineers @Academic AI Grant-funded GPUs shared without a spreadsheet. Training Infrastructure SREs @AI Labs Node health and failed jobs in one view, tied to the run they hit. GPU Cluster Architects @Cloud Providers Partition and share GPUs by policy. Distributed Systems Engineers @AI Labs Multi-node jobs on Slurm or Kubernetes, from one control plane. Benchmark Engineers @Semiconductor The same harness on every cluster you connect. Model Deployment Leads @Healthcare AI Each model version pinned and deployed under the same policy. HPC Systems Admins @National Lab Monitor jobs, queues, and node health in a single view. Research Software Engineers @University Ship reproducible environments instead of maintaining modulefiles. Computational Chemists @Pharma Queue docking campaigns from a template, with no scheduler expertise needed. Bioinformaticians @Genomics Run pipelines where the data already lives, in region. Simulation Leads @Aerospace Multi-node CFD and FEA on the Slurm workflows you already use. Climate Modelers @Public Sector Long multi-node runs on hardware you own, under one queue. Structural Analysts @Civil Engineering Solver jobs sized to the model, not to the queue. CFD Engineers @Motorsport Parameter sweeps that spill to cloud under the same rules. Molecular Dynamics Researchers @Biotech Long MD runs, tracked per project and per GPU-hour. Genomics Pipeline Engineers @Precision Medicine Sequencing pipelines on hardware you own, every run audited. Cryo-EM Scientists @Structural Biology Particle picking and refinement on GPUs you already own. Astrophysicists @Observatory Survey reductions on shared clusters, tracked per project. Seismic Imaging Engineers @Energy Full-waveform inversion on shared silicon. Reservoir Engineers @Oil and Gas History matching ensembles queued by policy. Weather Forecast Engineers @Meteorology Ensemble runs scheduled by priority on one queue. Materials Scientists @Advanced Manufacturing Whole DFT sweeps queued in one submission. Quantum Algorithm Researchers @Quantum Computing Classical simulation workloads on the same queue as everything else. Nuclear Engineers @Energy Monte Carlo transport with the audit trail attached. Plasma Physicists @Fusion Research Long particle-in-cell runs with node health in plain view. Computational Biologists @Pharma Scientists freed from ticket queues for compute. Protein Design Engineers @Biotech Folding and design runs on the same pool of GPUs. Drug Discovery Leads @Pharma Screening capacity that flexes with the program. Medical Imaging Researchers @Hospital Network Train on imaging data without ever moving it. Epidemiology Modelers @Public Health Scenario runs on shared capacity, with no ticket required. Geospatial Analysts @Defense Raster processing next to the archive, inside your boundary. Signal Processing Engineers @Aerospace and Defense Sensitive workloads inside your own boundary. Photonics Simulation Engineers @Semiconductor FDTD sweeps queued from templates, under one identity. CAD Engineers @Semiconductor Every EDA job tied to the engineer who ran it. Chip Verification Leads @Semiconductor Regression farms on shared clusters, with spend per project. Battery Simulation Engineers @Automotive Electrochemical models on shared GPUs, tracked per team. Aerodynamics Researchers @Aviation Mesh, solve, and post-process in one pipeline. Ocean Modelers @Marine Science Coupled ocean and atmosphere runs on one scheduler. Agricultural Data Scientists @AgTech Yield models trained on this season, during this season. PhD Researchers @Higher Education Run complex simulations through a simple, no-code interface. Quant Researchers @Capital Markets Burst risk and backtesting workloads without a procurement cycle. Storage Engineers @National Lab Attach the right filesystem to the right job, per policy. Data Engineers @Automotive Spark, Ray, and batch on shared silicon instead of separate stacks. Scheduler Administrators @Manufacturing Federate Slurm and Kubernetes queues under one set of rules. GPU Performance Engineers @Semiconductor See utilization per GPU and tune placement, sharing, and partitioning. Research IT Directors @Healthcare Unify hybrid compute under one auditable control plane. Operations Managers @FinTech Track real-time usage and spend by team. Procurement Directors @Energy See which GPUs sit idle before the next purchase order. Platform Engineers @SaaS Self-service compute with the guardrails already set. Site Reliability Engineers @Enterprise AI One dashboard for jobs, nodes, and spare capacity. Kubernetes Administrators @Retail GPU nodes scheduled by policy, not by annotation sprawl. Cloud Architects @Insurance The same control plane in every region you run in. Network Engineers @Data Centers See which jobs run on which nodes, across every cluster. Security Engineers @Defense Identity-based access to every node, and no shared keys. Compliance Officers @Banking Residency and retention enforced before a job runs. CISOs @Healthcare Prove where every workload ran, and on whose approval. FinOps Analysts @Media Chargeback by team, by project, by GPU-hour. Capacity Planners @Telecom Usage trends by team, before the next capacity request. Infrastructure Directors @Manufacturing Retire the second stack and keep both workloads. CTOs @Startups Buy the GPUs once, then use them like a cloud. VPs of Engineering @Enterprise AI Ship the roadmap without hiring a cluster team. Heads of AI @Retail Every team on one platform, and one budget view. Research Computing Leads @University Fair-share across departments, without the arguments. Grant Administrators @Research Institute Spend tied to the award it belongs to. Lab Managers @Biotech Onboard a new scientist with the sign-in they already have. Data Center Operators @Colocation Rent GPUs to tenants with isolation you can prove. Neocloud Operators @GPU Cloud Launch a managed HPC offering on your own hardware. Solutions Architects @Systems Integrators Reference architectures with identity and audit built in. Support Engineers @HPC Vendors See the failing job and the node behind it together. Product Managers @AI Platforms Ship features on infrastructure you never had to build. Sovereign Cloud Leads @Government AI workloads that stay inside national borders, by policy. Chief Data Officers @Public Sector One audit trail across every cluster, and who used each one. Our Blog ## Insights & Updates From the team building Vantage Compute, the secure control plane for sovereign AI and HPC. [October 19, 2026 ▦ Engineering](https://www.vantagecompute.ai/blog/vantage-post-quantum-security/) ### [Vantage Post-Quantum Security](https://www.vantagecompute.ai/blog/vantage-post-quantum-security/) [October 5, 2026 ▦ Company](https://www.vantagecompute.ai/blog/vantage-compute-joins-oregon-ai-accelerator/) ### [Vantage Compute Joins the Oregon AI Accelerator](https://www.vantagecompute.ai/blog/vantage-compute-joins-oregon-ai-accelerator/) [September 1, 2025 ▦ HPC](https://www.vantagecompute.ai/blog/choosing-hpc-workload-manager/) ### [Choosing the Right Workload Manager](https://www.vantagecompute.ai/blog/choosing-hpc-workload-manager/) [All Articles →](https://www.vantagecompute.ai/blog/)