Sovereign AI & HPC · Secure Control Plane

The 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.

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Vantage cluster list spanning AWS, Google Cloud, Azure, and on-prem — 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 — Open models, pinned and served on your clusters

Runs everywhere you do

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

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

Key and layers illustrating identity-aware access

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.

Book Demo

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 KubernetesOne control plane, one identity model, no refactoring.

Choose your cloudAWS, Azure, and Google Cloud, or your own datacenter.

UI, CLI, APIEvery action available to a person or a pipeline.

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

Apply one policy above both schedulers

DATA & WEIGHTS

Scale R&D securely

Grant mounts by policy, not by ticket

Vantage control plane

Run it in your cloud account or your own datacenter, reached by an outbound-only tunnel that opens no inbound ports

CLI

API

SAME BOUNDARY, ANY SUBSTRATE

+ 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

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

Security, Access, Control

UICLIAPI

Slurm

gpu-prod · AWS

512 × H200

Kubernetes

inference · Google Cloud

autoscale 0 → 128

SlurmKubernetes

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

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

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.

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

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.

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.

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.