Openstack HPC AI Data Center

Building an On-Premises AI Cloud: Integrating OpenStack and NVIDIA for High-Performance Workloads

Executive Summary

Artificial Intelligence (AI), Machine Learning (ML), and High-Performance Computing (HPC) require significant computational power, often accelerated by Graphics Processing Units (GPUs). While public cloud providers offer access to this technology, many enterprises need on-premises solutions to gain full control over their infrastructure, manage costs, and avoid vendor lock-in.

This white paper details how the open-source OpenStack platform can be integrated with NVIDIA's powerful GPUs to build a scalable, high-performance, and cost-effective AI cloud in a private data center. This powerful combination provides the necessary infrastructure to handle demanding workloads, from large-scale model training to multi-tenant inference, using both bare metal and virtualized environments.

The AI Infrastructure Challenge

Today's AI workloads are characterized by several key infrastructure demands:

• High computational requirements: Processing massive datasets and training complex models necessitates powerful, parallel processing hardware.

• Data sovereignty and security: Industries such as finance and healthcare have strict regulatory requirements that make moving sensitive data to a public cloud undesirable.

• Cost management: The pay-as-you-go model of public clouds can lead to unpredictable and escalating costs, especially for large, sustained AI projects.

• Performance optimization: Overcoming the overhead of virtualization and network latency is critical for optimizing the performance of distributed AI and HPC workloads.

  
  

How OpenStack and NVIDIA Deliver an AI Cloud

OpenStack is a modular, open-source cloud computing platform that provides Infrastructure-as-a-Service (IaaS) for provisioning and managing resources. By natively integrating with NVIDIA hardware, OpenStack allows organizations to build a comprehensive AI cloud tailored to their exact needs.

Flexible Hardware Access to NVIDIA GPUs

OpenStack provides administrators with granular control over how NVIDIA GPU resources are accessed and utilized. This flexibility allows for the optimization of different types of AI workloads:

• PCI Passthrough: For workloads requiring the highest possible performance and exclusive access to a GPU, OpenStack can be configured to use PCI passthrough. This method directly assigns a physical GPU to a single virtual machine (VM), eliminating the overhead of a hypervisor.

• Virtual GPU (vGPU): When multiple VMs need to share a single GPU, NVIDIA vGPU software can be utilized. An administrator can partition a physical GPU into multiple vGPUs, increasing resource utilization and enabling multi-tenant AI deployments.

• Multi-Instance GPU (MIG): Supported by NVIDIA Ampere (A100) GPUs and later, MIG takes vGPU partitioning a step further by offering hardware-enforced isolation. A single GPU can be securely partitioned into smaller, independent instances with guaranteed compute resources and memory.

• Bare Metal Provisioning (Ironic): For the most demanding AI workloads, OpenStack's Ironic project provisions and manages physical bare metal servers with GPUs. This bypasses any virtualization layer, ensuring maximum performance and control over the hardware.

  
  

Management and Orchestration with OpenStack Services

Multiple OpenStack services work together to build, manage, and scale the AI cloud environment:

• Nova (Compute): The core compute service, Nova, is configured to recognize and schedule GPU resources. Administrators can define "flavors" to specify the type and quantity of GPU access, ensuring VMs are placed on compute nodes with the correct hardware.

• Cyborg: As a specialized project for hardware accelerators, Cyborg can offer more flexible and advanced management of GPUs than the standard Nova integration, catering to complex and specific AI needs.

• Magnum: This service provisions and manages container orchestration engines like Kubernetes on OpenStack infrastructure. This enables the use of containers—a lightweight and portable method for packaging AI applications—on GPU-enabled hardware.

• Cinder and Swift (Storage): AI workloads generate and consume massive datasets. Cinder provides high-performance block storage, while Swift offers scalable object storage for handling large datasets and model checkpoints.

• InfiniBand Networking: For high-performance, distributed AI training and HPC workloads, OpenStack can integrate with NVIDIA's InfiniBand fabric. This provides the low-latency, high-bandwidth networking essential for large-scale GPU clusters to operate efficiently.

  
  

Real-World Success Stories

This integrated approach is already being used by forward-thinking enterprises and cloud providers to build high-performance AI data centers:

• FPT Smart Cloud: This prominent cloud provider leverages OpenStack to manage AI factories featuring NVIDIA H100 and H200 GPUs. They use a combination of OpenStack services, including Ironic for bare metal, Nova for virtual GPU instances, and Magnum for GPU-enabled Kubernetes clusters.

• CoreWeave: As a specialized cloud provider for compute-intensive AI workloads, CoreWeave uses powerful NVIDIA GPUs, including the H100, and builds sophisticated infrastructure solutions to deliver higher GPU cluster performance.

  
  

Key Benefits for AI Workloads

Deploying an AI cloud with OpenStack and NVIDIA offers compelling advantages for organizations:

• Cost-effectiveness: Eliminating reliance on expensive public cloud services results in significantly lower long-term costs and more predictable infrastructure spending.

• Flexibility and Customization: OpenStack's open-source nature allows for a highly customized AI platform, enabling fine-tuning of the environment to meet unique data pipelines and model requirements.

• Full-stack Control: Enterprises can avoid vendor lock-in and maintain complete visibility and control over their entire AI infrastructure stack, from the hardware to the orchestration layers.

• Performance Optimization: Support for bare metal provisioning and high-speed InfiniBand networking eliminates virtualization overhead, delivering top-tier performance for the most demanding AI and HPC workloads.

  
  

Future Trends in AI Infrastructure

As AI technology continues to evolve, so will the infrastructure that supports it. Here are some emerging trends to watch:

Increased Adoption of Edge Computing

The rise of IoT devices and the need for real-time data processing are driving the adoption of edge computing. This approach allows data to be processed closer to its source, reducing latency and bandwidth usage. OpenStack can play a crucial role in managing edge resources, ensuring seamless integration with centralized AI workloads.

Enhanced Security Measures

As AI becomes more prevalent, so do concerns about data security and privacy. Organizations will need to implement robust security measures to protect sensitive information. OpenStack's customizable architecture allows for the integration of advanced security protocols, ensuring compliance with industry regulations.

Evolution of AI Models

The complexity of AI models is increasing, requiring more sophisticated infrastructure. Organizations will need to invest in scalable solutions that can accommodate these evolving demands. OpenStack's flexibility allows for easy scaling of resources, making it an ideal choice for future-proofing AI infrastructure.

Conclusion

The integration of OpenStack and NVIDIA provides a robust, scalable, and cost-effective solution for building on-premises AI data centers. By combining OpenStack's open-source cloud management capabilities with NVIDIA's leading-edge GPU technology, enterprises gain the flexibility and control to manage their AI workloads with maximum efficiency and performance. This architecture empowers organizations to innovate faster, maintain data sovereignty, and optimize their AI infrastructure for long-term success.

For more information on how to implement these technologies, please visit UberZunn Cloud Consulting.