Clusters across every cloud and your own racksEvery distributed training run in one viewSlurm and Kubernetes schedulers, side by sideNotebooks on GPU clusters, in secondsOpen models, pinned and served on your clusters
Runs everywhere you do
The Vantage Control Plane
Virtually Limitless™ Compute.
Execute AI, HPC, and Quantum workloads across any environment.
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.
Slurm+Kubernetes Orchestration
Bring Slurm to Kubernetes
Run your existing HPC workloads without refactoring or compromise
Spin Up Clusters Instantly
Fully automated provisioning means you're up and running with just a few clicks
Identity-Aware Infrastructure
No More Integration Headaches
Secure, identity-based connectivity for your entire stack: Slurm, AI tools, and Kubernetes
IAM Simplified
Identity-based authentication eliminates passwords across compute, storage, and workloads
GPU-Native Platform Operations
Stay Secure, Stay Running
Active security monitoring and safe update management protect your GPU workloads
Get More from Your GPUs
Smart placement technology maximizes utilization through intelligent sharing and partitioning.
Vantage unifies Slurm and Kubernetes into a single, chip‑agnostic control plane.
Sovereign & Open
Your models. Your silicon. Your jurisdiction.
Take the open stack and run it on infrastructure you own. Keep the models, the silicon, and the data inside the jurisdiction you answer to.
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 + 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
UICLIAPI
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
mtlsworkload cert issued to notebook pod, rotated automatically
identitytoken validated at gateway, role researcher on project genomics
tunneloutbound session established, no inbound port opened
modeldigest sha256:9f2c… pinned from approved registry
deniedmount /phi refused, outside role's data scope
quotateam 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.
Vantage Compute, an NVIDIA Inception program member, is building the future compute layer with early access to the latest GPU platforms. 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
every team · every cluster · every workload
Provision clusters, manage schedulers, and track spend from one dashboard.
Operational Intelligence
Complete Cluster Control
Via UI, CLI, or API.
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.
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
Attribute infrastructure 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.
One hundred job titles, one platform: notebooks, batch jobs, distributed training, and large-scale simulation across AI, HPC, quantum, and enterprise compute.
AI Researchers @Life Sciences
Launch experiments instantly, no IT provisioning required.
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
Thousand-GPU runs that checkpoint and resume on their own.
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
Feed multi-camera training sets straight off cold storage.
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 that fit inside the maintenance window.
Reinforcement Learning Researchers @Robotics
Thousands of parallel rollouts under one scheduler.
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
Build centrally, then ship models to the whole edge fleet.
AI Product Engineers @Startups
Stand up an inference endpoint the week you need it.
LLM Ops Engineers @Enterprise AI
Watch token throughput per tenant, not per guess.
Synthetic Data Engineers @Autonomous Systems
Generate corpora at cluster scale, not laptop scale.
Vector Search Engineers @Search
Index rebuilds that never stall a live query.
Feature Platform Engineers @FinTech
One feature store serving batch and online alike.
Model Risk Analysts @Banking
Every run auditable, from the dataset to the weights.
AI Governance Leads @Public Sector
Residency and approval enforced before the job starts.
Research Engineers @Academic AI
Grant-funded GPUs shared without a spreadsheet.
Training Infrastructure SREs @AI Labs
Failed nodes drain and requeue without a page.
GPU Cluster Architects @Cloud Providers
Partition, share, and oversubscribe by policy.
Distributed Systems Engineers @AI Labs
Fabric topology handled for you, not hand-tuned per job.
Benchmark Engineers @Semiconductor
The same harness across every silicon vendor.
Model Deployment Leads @Healthcare AI
Canary each model version behind the same gate.
AI Researchers @Life Sciences
Launch experiments instantly, no IT provisioning required.
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
Thousand-GPU runs that checkpoint and resume on their own.
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
Feed multi-camera training sets straight off cold storage.
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 that fit inside the maintenance window.
Reinforcement Learning Researchers @Robotics
Thousands of parallel rollouts under one scheduler.
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
Build centrally, then ship models to the whole edge fleet.
AI Product Engineers @Startups
Stand up an inference endpoint the week you need it.
LLM Ops Engineers @Enterprise AI
Watch token throughput per tenant, not per guess.
Synthetic Data Engineers @Autonomous Systems
Generate corpora at cluster scale, not laptop scale.
Vector Search Engineers @Search
Index rebuilds that never stall a live query.
Feature Platform Engineers @FinTech
One feature store serving batch and online alike.
Model Risk Analysts @Banking
Every run auditable, from the dataset to the weights.
AI Governance Leads @Public Sector
Residency and approval enforced before the job starts.
Research Engineers @Academic AI
Grant-funded GPUs shared without a spreadsheet.
Training Infrastructure SREs @AI Labs
Failed nodes drain and requeue without a page.
GPU Cluster Architects @Cloud Providers
Partition, share, and oversubscribe by policy.
Distributed Systems Engineers @AI Labs
Fabric topology handled for you, not hand-tuned per job.
Benchmark Engineers @Semiconductor
The same harness across every silicon vendor.
Model Deployment Leads @Healthcare AI
Canary each model version behind the same gate.
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 thousands of docking runs without touching a scheduler.
Bioinformaticians @Genomics
Run pipelines where the data already lives, in region.
Simulation Leads @Aerospace
Scale multi-node CFD and FEA with zero license friction.
Climate Modelers @Public Sector
Long multi-node runs with checkpoints and restart built in.
Structural Analysts @Civil Engineering
Solver jobs sized to the model, not to the queue.
CFD Engineers @Motorsport
Overnight sweeps finished before the morning review.
Molecular Dynamics Researchers @Biotech
Microsecond trajectories without babysitting the run.
Genomics Pipeline Engineers @Precision Medicine
Throughput that keeps pace with the sequencer.
Cryo-EM Scientists @Structural Biology
Particle picking and refinement on GPUs you already own.
Astrophysicists @Observatory
Survey reductions that keep pace with the telescope.
Seismic Imaging Engineers @Energy
Full-waveform inversion on shared silicon.
Reservoir Engineers @Oil and Gas
History matching in hours instead of over the weekend.
Weather Forecast Engineers @Meteorology
Ensembles that hit the delivery window every cycle.
Materials Scientists @Advanced Manufacturing
Whole DFT sweeps queued in one submission.
Quantum Algorithm Researchers @Quantum Computing
Classical simulation and QPU access in one workflow.
Nuclear Engineers @Energy
Monte Carlo transport with the audit trail attached.
Plasma Physicists @Fusion Research
Long particle-in-cell runs that survive node loss.
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 published the day they are asked for.
Geospatial Analysts @Defense
Petabyte raster processing next to the archive.
Signal Processing Engineers @Aerospace and Defense
Classified workloads inside your own boundary.
Photonics Simulation Engineers @Semiconductor
FDTD sweeps that never stall on a license.
CAD Engineers @Semiconductor
Ensure license availability: zero denials, zero overspending.
Chip Verification Leads @Semiconductor
Regression farms that finish before tapeout.
Battery Simulation Engineers @Automotive
Electrochemical models run at fleet scale.
Aerodynamics Researchers @Aviation
Mesh, solve, and post-process in one pipeline.
Ocean Modelers @Marine Science
Coupled runs that restart exactly where they stopped.
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 thousands of docking runs without touching a scheduler.
Bioinformaticians @Genomics
Run pipelines where the data already lives, in region.
Simulation Leads @Aerospace
Scale multi-node CFD and FEA with zero license friction.
Climate Modelers @Public Sector
Long multi-node runs with checkpoints and restart built in.
Structural Analysts @Civil Engineering
Solver jobs sized to the model, not to the queue.
CFD Engineers @Motorsport
Overnight sweeps finished before the morning review.
Molecular Dynamics Researchers @Biotech
Microsecond trajectories without babysitting the run.
Genomics Pipeline Engineers @Precision Medicine
Throughput that keeps pace with the sequencer.
Cryo-EM Scientists @Structural Biology
Particle picking and refinement on GPUs you already own.
Astrophysicists @Observatory
Survey reductions that keep pace with the telescope.
Seismic Imaging Engineers @Energy
Full-waveform inversion on shared silicon.
Reservoir Engineers @Oil and Gas
History matching in hours instead of over the weekend.
Weather Forecast Engineers @Meteorology
Ensembles that hit the delivery window every cycle.
Materials Scientists @Advanced Manufacturing
Whole DFT sweeps queued in one submission.
Quantum Algorithm Researchers @Quantum Computing
Classical simulation and QPU access in one workflow.
Nuclear Engineers @Energy
Monte Carlo transport with the audit trail attached.
Plasma Physicists @Fusion Research
Long particle-in-cell runs that survive node loss.
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 published the day they are asked for.
Geospatial Analysts @Defense
Petabyte raster processing next to the archive.
Signal Processing Engineers @Aerospace and Defense
Classified workloads inside your own boundary.
Photonics Simulation Engineers @Semiconductor
FDTD sweeps that never stall on a license.
CAD Engineers @Semiconductor
Ensure license availability: zero denials, zero overspending.
Chip Verification Leads @Semiconductor
Regression farms that finish before tapeout.
Battery Simulation Engineers @Automotive
Electrochemical models run at fleet scale.
Aerodynamics Researchers @Aviation
Mesh, solve, and post-process in one pipeline.
Ocean Modelers @Marine Science
Coupled runs that restart exactly where they stopped.
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
Maximize GPU efficiency and eliminate budget waste.
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
InfiniBand and Ethernet fabrics mapped to the jobs on them.
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
See next quarter’s shortfall this quarter.
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 in a morning.
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 that deploy exactly as written.
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
Run frontier workloads entirely inside national borders.
Chief Data Officers @Public Sector
One inventory of data, compute, and who touched both.
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
Maximize GPU efficiency and eliminate budget waste.
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
InfiniBand and Ethernet fabrics mapped to the jobs on them.
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
See next quarter’s shortfall this quarter.
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 in a morning.
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 that deploy exactly as written.
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
Run frontier workloads entirely inside national borders.
Chief Data Officers @Public Sector
One inventory of data, compute, and who touched both.
Our Blog
Insights & Updates
From the team building Vantage Compute, the modern compute layer for AI, HPC, and quantum.
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