Zadara is
NVIDIA AI Cloud Ready
Turn NVIDIA Accelerated Infrastructure Into a Secure, Multi-tenant Sovereign AI Cloud.
Zadara AI Cloud is validated under the NVIDIA AI Cloud Ready initiative, a technical validation framework for software stacks deployed on NVIDIA Cloud Partners. The validation assesses AI infrastructure software across networking, compute, orchestration, and AI platform layers for production-scale AI cloud deployments.
With Zadara AI Cloud, deploy NVIDIA accelerated infrastructure as a secure, multi-tenant sovereign AI cloud without having to build and integrate the cloud operating stack yourself.
Turn NVIDIA Infrastructure Into a Complete AI Cloud.
NVIDIA provides the accelerated infrastructure. Zadara provides the cloud operating layer.
Deploy NVIDIA infrastructure as a secure, sovereign, multi-tenant AI cloud without building and integrating the cloud stack yourself. Zadara AI Cloud unifies GPU and compute, networking, storage, PaaS, security, multi-tenancy, and automation into a single platform built to provision, manage, and operate NVIDIA AI infrastructure at scale.
From NVIDIA infrastructure to production AI, through one unified cloud platform.
Validated Across the Full NVIDIA Stack
NVIDIA AI Cloud Ready status looks at the whole solution: compute, networking, data platform, storage, and GPU working together as one validated system, not five separate procurement decisions.
Compute Platforms
- NVIDIA DGX
- OEM HGX servers
(e.g Lenovo, SuperMicro, Del) - NVL72 rack-scale systems
GPU Generations
- Ada Lovelace Architecture (e.g., L40, L40S)
- Hopper Architecture (e.g., H100, H200)
- Blackwell Architecture (e.g., B200, B300, RTX6000 PRO)
- Grace Hopper Superchips (e.g., GH100, GH200)
- Grace Blackwell Superchips (e.g., GB300)
- Vera Rubin Architecture
Network Fabrics
- NVIDIA Spectrum-X Ethernet
- NVIDIA InfiniBand
- Scalable Units (SU)
- SuperPOD
Footnote
Zadara-managed AI clouds run anywhere from 16 to thousands of GPUs on this same validated stack, on an NVIDIA Scalable Unit or a full Rack Scale deployment.
Build More Than AI Infrastructure
NVIDIA AI Cloud Ready
Build on a platform validated for the NVIDIA AI cloud ecosystem and designed to support NVIDIA AI Factory architectures.
Sovereign AI
Keep full control of your infrastructure, data, models, and workloads.
Secure Multi-Tenancy
Isolate customers, projects, and workloads across compute, networking, and storage, not just the orchestration layer.
Hyperscaler Experience
Familiar cloud operations on infrastructure you control: EC2-compatible APIs, automation, and Terraform support.
Better GPU Economics
Dynamically allocate GPUs, improve utilization, and turn expensive infrastructure into revenue-generating services.
Real Sovereignty. Built In.
For a growing number of operators, the question isn’t just “can I run this AI workload”;
it’s “can I guarantee where the data, the model, and the pipeline that touches them actually live, and who can reach them.”
Data Locality & Residency
Training data, inference results, model weights, and telemetry never leave defined geographic or jurisdictional boundaries.
Operational Control
Full autonomy over who accesses infrastructure, when upgrades happen, and where workloads run.
Compliance From Day One
Architecture designed to support regional sovereignty mandates without custom work per jurisdiction.
500+
Sovereign cloud locations operated globally.
One Platform. Multiple AI Opportunities.
For Service Providers
Turn NVIDIA infrastructure into a revenue-generating AI cloud. Launch GPU and AI services, support multiple customers, and automate provisioning and consumption tracking from a centralized platform.
For Enterprises
Bring cloud operations to your infrastructure without giving up control. Build secure AI environments for training, inference, RAG, and emerging AI workloads while maintaining control over critical AI assets.
For OEMs & Integrators
Turn NVIDIA infrastructure deployments into complete AI cloud opportunities by adding the operational cloud layer customers need to turn hardware into usable AI services.
From NVIDIA Infrastructure
to AI Outcomes
Frequently Asked Questions
What is sovereign AI?
Sovereign AI is the ability to develop, run, and control AI systems (including the data, models, and infrastructure behind them) entirely within a defined jurisdictional or organizational boundary, without depending on an external provider for access or control.
Sovereign cloud vs. private cloud: what’s the difference?
A private cloud describes dedicated, single-tenant infrastructure. A sovereign cloud adds a further requirement: that data, operations, and control stay within defined legal and geographic boundaries, regardless of who owns or operates the infrastructure.
What is a GPU cloud?
A GPU cloud provisions GPU compute (for AI training, inference, and other accelerated workloads) as a managed, on-demand service rather than infrastructure customers build and operate entirely themselves.
What does NVIDIA AI Cloud Ready Initiative status mean?
NVIDIA AI Cloud Ready Initiative validation confirms that Zadara’s zCompute orchestration infrastructure and data platform meet NVIDIA’s technical requirements for running production AI workloads on NVIDIA-accelerated infrastructure. This validation covers the complete stack (compute, networking, storage, and GPU orchestration) working together as a unified, validated system rather than separate components operators must integrate themselves.
The validation applies to current and next-generation NVIDIA hardware including DGX/HGX servers, NVL72 GB200/GB300 rack-scale systems, and upcoming Vera Rubin platforms across both NVIDIA Spectrum-X Ethernet and InfiniBand networking fabrics. It’s part of the NVIDIA Cloud Partner (NCP) program, which recognizes solutions designed to accelerate AI factory deployments and AI cloud operations.
How quickly can an AI cloud go from hardware delivery to production workloads?
With Zadara’s NVIDIA-validated stack, the timeline compresses significantly compared to custom integration projects:
- Build: Hardware arrives, GPU servers are racked, cabled, and powered; networking is validated against NVIDIA reference architecture. Zadara AI Cloud control plane arrives and is stacked.
- Cloudify & Launch: The cloud becomes operational: Zadara AI Cloud provisions GPU nodes, tenants onboarded with VPC-style isolation, users and AI practitioners are introduced, self-serving instances are accessible through a single management interface.
- Run, Always On: Follow-the-sun support, automated monitoring, non-disruptive upgrades, and built-in disaster recovery.
The critical difference: integration work between compute, networking, storage, data platform, and GPU orchestration has been pre-validated by NVIDIA; operators inherit tested configurations rather than spending months on custom integration.
The benefit you’d get is a shorter time to value; go from months to days.
What NVIDIA hardware architectures does Zadara support?
What NVIDIA hardware architectures does Zadara support?
Compute Systems: NVIDIA DGX and HGX servers; NVIDIA Scalable Unit racks; NVL72 GB200 and GB300 rack-scale systems; upcoming Vera Rubin platforms.
GPU Accelerators: L40, L40S (inference and mixed workloads); H100, H200 (large-scale training and high-performance inference); B200, B300 (next-generation model development and production), and the Vera Rubin platform
Networking Fabrics: NVIDIA Spectrum-X Ethernet; InfiniBand for ultra-low-latency GPU-to-GPU communication.
Deployments scale from a fewGPUs in edge AI configurations to tens of thousands of GPUs in full AI factory installations on the same validated stack; operators can start small and grow to hyperscale without changing operational models or management tools.
Can Zadara run AI alongside general purpose application workloads on the same infrastructure?
Can Zadara run AI alongside general purpose application workloads on the same infrastructure?
Yes. Zadara’s architecture supports AI training, inference, RAG applications, and agentic workloads alongside general-purpose compute, disaster recovery, and traditional enterprise applications under unified management, which maximizes GPU utilization during off-peak AI training windows and improves overall infrastructure ROI. The same multi-tenancy isolation that separates AI customers or business units also separates AI workloads from general-purpose compute.