Building AI Infrastructure
Deploy private AI with integrated compute, GPU resources, storage, virtualization, networking, and operations on hardware you control.
Keep Data Private
Run models and sensitive datasets inside infrastructure governed by your organization, not a shared public AI platform.
Use GPUs Better
Consolidate training and inference workloads to improve GPU accessibility and reduce isolated, underused accelerator resources.
Scale Without Lock-In
Expand using standard x86 infrastructure from preferred vendors instead of committing every upgrade to one proprietary platform.
Turn AI Spending Into Usable Capacity
Build an environment where expensive compute, storage, and GPU resources work together instead of becoming isolated investments.
Faster Deployment
Faster Deployment
Independent Resource Scaling
Launch private AI environments without separately integrating storage, virtualization, compute management, and infrastructure operations.
Put Resources To Work
Put Resources To Work
Better GPU Utilization
Consolidate workloads across available compute and GPU infrastructure instead of dedicating separate systems to every application.
Controlling Entire Stack
Controlling Entire Stack
Flexible Hardware Choice
Choose how AI data, workloads, hardware, and infrastructure are deployed, managed, protected, and expanded.
Faster Deployment
Launch private AI environments without separately integrating storage, virtualization, compute management, and infrastructure operations.
Put Resources To Work
Consolidate workloads across available compute and GPU infrastructure instead of dedicating separate systems to every application.
Controlling Entire Stack
Choose how AI data, workloads, hardware, and infrastructure are deployed, managed, protected, and expanded.
Faster Deployment
Independent Resource Scaling
Launch private AI environments without separately integrating storage, virtualization, compute management, and infrastructure operations.
Problems We Solve With AI Infrastructure Solutions
GPU Capacity Bottlenecks
Give training and inference workloads access to shared, centrally managed GPU-enabled infrastructure.
Slow Data Pipelines
Deliver scalable storage services for training data, checkpoints, models, vector databases, and analytics.
Expensive Platform Lock-In
Avoid infrastructure designs that restrict future expansion to one vendor’s appliances, licensing, or ecosystem.
Disconnected AI Systems
Bring storage, virtualization, compute, networking, and operations together under one software-defined foundation.
Unpredictable Scaling Costs
Expand storage and compute according to actual workload demands instead of replacing complete infrastructure stacks.
Private AI Complexity
Operate controlled AI environments without stitching together separate platforms for data, GPUs, workloads, and management.
GPU Capacity Bottlenecks
Give training and inference workloads access to shared, centrally managed GPU-enabled infrastructure.
Slow Data Pipelines
Deliver scalable storage services for training data, checkpoints, models, vector databases, and analytics.
Expensive Platform Lock-In
Avoid infrastructure designs that restrict future expansion to one vendor’s appliances, licensing, or ecosystem.
Disconnected AI Systems
Bring storage, virtualization, compute, networking, and operations together under one software-defined foundation.
Unpredictable Scaling Costs
Expand storage and compute according to actual workload demands instead of replacing complete infrastructure stacks.
Private AI Complexity
Operate controlled AI environments without stitching together separate platforms for data, GPUs, workloads, and management.
GPU Capacity Bottlenecks
Give training and inference workloads access to shared, centrally managed GPU-enabled infrastructure.
Slow Data Pipelines
Deliver scalable storage services for training data, checkpoints, models, vector databases, and analytics.
Expensive Platform Lock-In
Avoid infrastructure designs that restrict future expansion to one vendor’s appliances, licensing, or ecosystem.
Disconnected AI Systems
Bring storage, virtualization, compute, networking, and operations together under one software-defined foundation.
Unpredictable Scaling Costs
Expand storage and compute according to actual workload demands instead of replacing complete infrastructure stacks.
Private AI Complexity
Operate controlled AI environments without stitching together separate platforms for data, GPUs, workloads, and management.
Centralized Infrastructure Management
SteelDome gives organizations architectural flexibility without forcing them to sacrifice integration, scalability, or operational control.
Hardware Independence
Run SteelDome on supported standard x86 infrastructure instead of purchasing a mandatory proprietary appliance stack.
Flexible Architecture
Deploy dedicated storage, hyperconverged infrastructure, or hybrid environments based on workload and scaling requirements.
Turnkey Availability
Choose validated Supermicro configurations when faster procurement and integrated deployment are more important than hardware selection.
Unified Operations
Use StratiSYSTEM™ OS to deploy, manage, operate, and orchestrate the complete AI infrastructure environment.