Public cloud hyper-scalers charge astronomical margins on GPU time, lock you into proprietary APIs, and claim ownership of your data parameters. OpenML OpenStack delivers enterprise-grade, bare-metal high-performance virtualization and multi-tenant isolation, automated specifically for training, hosting, and scaling ML workloads.
Compatible & Backed by Industry Standards
# Registering Sovereign GPU Cloud Node
$ openstack host register --gpu-profile h100-sxml8
SUCCESS: Host node-04 attached to Nova scheduler.
# Query Swift storage cluster status
$ swift-ring-builder object.builder write_ring
Ring file updated. Replication health: 100%.
Sovereignty Guaranteed: Your model weights stay in local NVMe block storage. Zero outbound egress penalties.
Sovereign AI is about control, data protection, and absolute economics. Renting instances on public clouds is fine for small projects, but scaling models from the ground up requires infrastructure autonomy.
Under modern AI regulations (like the EU AI Act), storing proprietary medical, financial, or state training data in foreign hyper-scaler databases is a compliance minefield. OpenML OpenStack operates in your physical data centers or trusted national colocation sites.
Public clouds charge an average of 300% markup on GPU hardware costs and penalize you with steep egress charges when downloading trained model weights. A private OpenStack bare-metal network amortizes hardware costs to a fraction of SaaS prices.
Containers alone can't orchestrate hyper-scale environments. OpenStack allows you to spin up high-throughput InfiniBand routing, assign physical GPU pass-through directly to compute environments, and partition nodes with total hardware-level isolation.
OpenML organizes individual OpenStack components into a logical lifecycle stack. Rather than deploying everything blindly, each layer functions both as a standalone operational product and a unified cloud foundation.
Click on the interactive layers on the right to drill down into the specific component, its function, and why it is essential to building an enterprise AI infrastructure.
The bedrock foundation of AI infrastructure. Automatically provisions operating systems directly to physical bare-metal hardware. Zero hypervisor performance penalty for heavy GPU training clusters.
Compare the real estimated cost of renting H100 GPU compute and Object storage on commercial public hyper-scalers (AWS/Azure) versus hosting them in a private OpenML OpenStack Sovereign cloud deployment.
Define your AI cloud target specifications below, and our configuration parser will instantly map out your core OpenStack network, hardware segment recommendations, and deployment commands.
LLM fine-tuning demands direct memory access with InfiniBand controllers and high-throughput localized NVMe swap spaces.
# kolla-ansible custom Swift layout for LLM Clusters
enable_swift: "yes"
swift_enable_tempauth: "no"
swift_keystone_user_role: "admin"
# GPU mapping configuration
nova_cell_gpus_enable: "yes"
ironic_dns_integration: "yes"
These snippets represent actual configuration commands recommended by the upstream OpenStack documentation to instantiate this specific environment.
Don't let OpenStack's raw configuration complexity intimidate you. Partner with OpenML cloud engineers to design, build, and support your custom private sovereign cluster.
Thank you for your interest in the OpenML OpenStack initiative. An experienced private cloud architect will reach out to you via email within 24 hours with your customized infrastructure layout diagram.