As artificial intelligence (AI) continues to transform industries, enterprises seek more cost-efficient, secure, and scalable ways to run inference workloads. Public cloud services offer flexibility but come with concerns over costs, data privacy, and governance. VMware Private AI Foundation with NVIDIA delivers an on-premise alternative, combining VMware Cloud Foundation (VCF) with NVIDIA AI Enterprise, designed for high-performance AI inference workloads using NVIDIA HGX systems.
Why Enterprises Need Private AI Infrastructure
GPU Optimization Challenges: On-prem GPUs often suffer from underutilization due to misallocation or overprovisioning. VMware’s platform enables dynamic GPU allocation, ensuring maximum utilization and efficiency.
Cloud-Like Flexibility for Data Scientists: The fast-evolving AI landscape requires a seamless, flexible environment for data scientists while IT teams retain control over infrastructure.
Data Privacy and Governance: As AI models rely on sensitive data, private AI solutions ensure security, compliance, and controlled access to proprietary models and datasets.
Familiar VMware Management Interface: IT administrators can leverage VMware’s widely used management tools, reducing learning curves and operational overhead.
The Core Components of VMware Private AI Foundation with NVIDIA
VMware Cloud Foundation (VCF): A full-stack private cloud platform integrating vSphere, vSAN, NSX, and the Aria Suite.
NVIDIA AI Enterprise: Includes NVIDIA vGPU (C-Series), NIM microservices, NeMo Retriever, and AI Blueprints, optimizing AI workloads.
HGX Systems: NVIDIA-certified servers featuring 8x H100/H200 GPUs, interconnected via NVSwitch and NVLink, delivering industry-leading performance.
Ethernet Networking: High-speed networking with NVIDIA Spectrum-X Ethernet ensures fast, efficient data transfer between nodes.
Reference Architecture for AI Inference
The architecture is designed for enterprises deploying AI workloads in private data centers. Key elements include:
1. Physical Architecture
Inference Servers: 4–16 NVIDIA HGX systems with H100/H200 GPUs
Networking: 100 GbE Ethernet fabric for inference workloads and 25 GbE for management and storage
Management Servers: 4 vSAN-ready nodes hosting VMware’s core infrastructure
2. Virtual Architecture
Management Domain: Manages the private cloud environment, including SDDC Manager, vCenter, NSX, and Aria Suite.
Workload Domain: Hosts AI workloads, leveraging Supervisor Clusters to deploy Deep Learning Virtual Machines (DLVMs) and AI Kubernetes clusters.
Vector Databases: PostgreSQL with pgVector extension enables retrieval-augmented generation (RAG) for generative AI applications.
Performance & Validation
VMware and NVIDIA validate the solution’s performance using GenAI-Perf benchmarking, comparing virtualized environments with bare-metal deployments. The optimized platform delivers high throughput and low latency, ensuring scalable, cost-effective AI inference.
Why Choose VMware Private AI Foundation with NVIDIA?
✅ Enhanced GPU Utilization: Maximizes AI compute resources
✅ Enterprise-Grade Security: Ensures data privacy and model governance
✅ Operational Efficiency: Uses familiar VMware management tools
✅ Scalable & Future-Proof: Designed for evolving AI workloads
Final Thoughts
For enterprises looking to deploy AI inference workloads while maintaining control, security, and efficiency, VMware Private AI Foundation with NVIDIA provides a powerful, flexible, and cost-effective private AI infrastructure.
Ready to optimize your AI strategy? Contact VMware and NVIDIA for deployment guidance today!
