Unified AI Infrastructure Control
AI Controller
Deploy, monitor, and scale AI workloads across GPUs, clusters, and hybrid environments with an operator-focused control plane.
Simplified Datacenter Operations
Empower infrastructure and AI teams with a management layer designed for GPU-aware orchestration, governance, and visibility.
NVIDIA NIM Microservices Integrated
Launch optimized inference pipelines with NVIDIA NIM microservices. AI Controller automates deployment, scaling, and health monitoring so production teams can focus on customer experiences.
Deploy Hugging Face Models in Minutes
Pull from thousands of open-source models and deploy to your secure GPU appliance. Auto-tuned runtime images keep your teams focused on evaluating model quality rather than platform plumbing.
No-Code Fine-Tuning & RAG Workflows
Fine-tune foundation models with domain datasets, configure vector search, and ship Retrieval-Augmented Generation experiences without writing boilerplate orchestration code.
Managed AI/ML Package Lifecycle
Keep runtime environments consistent across teams with automated dependency resolution, version pinning, and proactive updates for your deep learning toolchain.
Easy Installation & Bring-Up
Follow streamlined installation guides and bootstrap fleets with automated discovery of supported GPUs, drivers, and CUDA libraries.
Minimum System Requirements
- • NVIDIA GPU with virtualization support
- • 128 GB system memory or higher
- • 8 CPU cores (16 threads recommended)
- • Ubuntu 20.04 LTS or newer
- • Python 3.9 runtime or above
