Sandbox
An engineering team wants to test an AI assistant on approved sample questions before using it in everyday work.
What would make the experiment useful?
Storm AI Factory™
Private AI, with the infrastructure already integrated.
Run training, inference, and agents in a customer-controlled AI system. Storm AI Factory™ brings open-source AI software, the infrastructure it needs, and Storm™ cluster management together in one appliance.
Deploy on premises or in colocation. Workloads use your assigned capacity, without per-token metering.
Workload examples
Start with the AI workflow.
Begin with a task your team needs to accomplish. These examples help define the models, tools, data, and capacity your AI Factory needs.
Train
A team needs to run a model-training job with an approved dataset and an allocation of compute and storage.
What data and capacity does the job need?
Inference
An application team wants to use a model to classify incoming documents, with capacity sized for the arriving requests.
How many requests must it serve, and how quickly?
Agents
A team wants an agent to gather information from approved sources and draft a report for a person to review.
Which actions can proceed, and which need a person's approval?
What's included
Software and infrastructure, delivered together.
An AI application depends on its operating system, drivers, storage, and services working together. The appliance includes that infrastructure foundation and the software to manage it throughout its supported lifecycle.
AI software for your workflows
An integrated, open-source software foundation for sandboxing, training, inference, and agents. The selected configuration defines the supported models, runtimes, tools, and data connections.
Infrastructure sized as a system
Compute, accelerator memory, storage, and connectivity selected together around your workload. Size the appliance for inference, training, or both.
Storm™ management included
Infrastructure provisioning and ongoing configuration are part of the appliance. Storm™ coordinates the underlying services, their dependencies, and cluster state.
Control and usage
Your environment. Your assigned capacity.
Connect AI to approved applications and data sources, with access and operating responsibilities defined for your deployment. Plan usage around the infrastructure assigned to that environment.
Match capacity to demand.
Size compute and storage around model requirements, concurrent users, and training jobs. Your subscription scope and any separately licensed software or services still apply.
Capacity planning includes provisioning, maintenance, and requalification alongside inference demand and training jobs.
Define data and operating controls
The environment specification identifies where source data, model artifacts, prompts, outputs, logs, and retained state can live and move. It also defines identities, permitted connections, administrative paths, and responsibility for access, monitoring, change, updates, incidents, backup, restoration, and retirement.
A system that can evolve
Keep infrastructure aligned with AI.
New models, applications, and demand change what the system needs. The included Storm™ management carries the supported infrastructure configuration through those changes, coordinating operating systems, drivers, storage, and services as one environment.
Evolve the software environment
Select supported models, runtimes, and applications as your AI requirements change. Storm™ calculates and applies the underlying service configuration from its definitions, dependencies, and required state.
Introduce qualified capacity
Add supported compute or storage around the next workload requirement. Qualify hardware, firmware, drivers, storage paths, and services together before putting the changed environment into use.
How Storm™ manages the infrastructure
Storm™ uses out-of-band control to configure supported bare metal outside the workload execution path. It provisions the underlying environment and maintains requested and observed cluster state. The selected configuration defines hardware support, maintenance procedures, and validation before changed resources return to service.
Within CloudSpawn™, Storm AI Factory™ can use supported bare-metal compute and accelerators, high-performance fabrics, and parallel-file-system or shared-root-storage patterns. This architecture is designed to preserve bare-metal performance characteristics; capacity, topology, and measured results follow the selected configuration.
Already operate an HPC or AI cluster? Storm™ is also available as cluster-management software and can run AI applications without the AI Factory appliance. Explore Storm™ for your cluster
Deployment choices
Place AI alongside your work.
Deploy the standalone appliance on premises or in colocation, alongside your existing compatible infrastructure. The deployment plan defines capacity, approved data connections, access controls, and support responsibilities.
Also available within NetThunder platforms.
Storm AI Factory™ is the AI operations module included with CloudSpawn™ and optional with Spark™ and SecureEnclave™. Choose the platform whose broader operating model fits your workload.
Spark™
Available in selected configurations. Run private AI on one supported node when the workflow fits its capacity and operating boundary.
Review Spark™SecureEnclave™
Available in selected configurations. Place approved private AI inside assessment scope, or connect through an explicitly approved integration path.
Review SecureEnclave™CloudSpawn™
Included. Run AI on qualified bare-metal capacity inside CloudSpawn™'s zero-trust tenant boundary, with tenant-held keys and root control. Provider management is excluded from tenant data, credentials, and workloads after handoff.
Review CloudSpawn™Supported models, runtimes, data services, topology, capacity, performance, isolation, and operations differ by platform and configuration.
Architecture review
Start with the AI task.
Bring the task, its data needs, and where you want the system to run. The first conversation identifies an appliance configuration or platform deployment to explore, open questions, and the next design decisions.