Deploying large-scale AI infrastructures comes with significant complexity for NVIDIA Cloud Providers (NCPs) who need to validate intricate, multi-tier network architectures built with NVIDIA’s state-of-the-art GPU and networking technologies.

In large-scale deployments, NCPs manage thousands of GPU nodes connected through multi-layered networking that includes leaf, spine, and super spine switches. Nvidia also recommends a Reference Architecture (RA) for NCPs to ensure that these configurations help achieve optimal throughput and low latency. However, implementing this design and validating the configurations is a challenge because of existing hardware limitations in testing environments, risking service reliability and deployment timelines.

The need for robust validation is crucial. The complexity of these topologies, coupled with multi-tenancy requirements, makes a reliable and scalable validation solution a necessity. The alternatives are performance degradation or network downtime, both of which are catastrophic in terms of revenue loss or SLA violation penalties.

To tackle these challenges, aarna.ml presents an innovative Digital Twin solution, which works seamlessly with NVIDIA Air to simplify network validation and streamline operations. This blog will provide an in-depth look at the importance of a Digital Twin, the common challenges faced by NCPs, and how the aarna solution can transform network deployment and management.

Typical Large Scale AI Cloud deployment

The diagram below depicts a high level topology of a large scale deployment.

Typical deployments comprise multiple nodes grouped under scalable units (SU). GPUs within these SUs are then connected through multi-tier switches comprising leaf and spine and core switches, such that GPU to GPU communications across the complete data center is optimized with minimal hops and thereby ensuring high performance and low latency. Please note that topology specified above is only for reference and does not indicate any recommended deployment configurations.

Challenges in Current AI Cloud Deployments and Operations

As we have seen above, the sheer scale of deployment, lack of adequate hardware resources for testing, absence of automation tools makes it difficult for NCPs to ensure that actual deployment matches the intended designed deployment. The current challenges that NCPs need to address could be categorized under day 0; day 1 activities and day 2 activities are also a key consideration and are detailed below.

Day 0 and Day 1 Design and Validation:

  • Manual Setup: Traditional methods involve time-consuming manual configuration of underlay networks and the testing of network cabling and server deployments. The entire lifecycle to make the deployment live could span a few months.
  • RA Compliance: Ensuring that the deployment aligns with NVIDIA’s RA specifications is challenging without a standardized validation tool and test and configuration scripts, increasing the potential for errors.
  • Hardware Limitations: Testing expansive, multi-tier topologies in lab settings is constrained by limited resources, leading to incomplete validation.

Day 2 Operations and Management:

  • Configuration management: Ensuring synchronized and versioned configurations across hundreds of switches is a complex task. Configuration drifts can lead to inconsistent network behavior and potential service issues.
  • Tenant Life Cycle Management (LCM): Allocating and deallocating nodes and virtual resources for tenants need overlay configurations to be performed on several switches and nodes. Manual approach and validating these on production set-up requires a careful design and implementation. Errors can be costly.
  • Topology Changes: Routine maintenance, switch replacements, and topology updates necessitate quick, error-free configuration updates to maintain network stability.
  • Reducing MTTR: Identifying and  correcting GPU related errors is time consuming because of manual steps and configurations. This can cause long mean-time-to-repair (MTTR) durations.

Introducing the Digital Twin Solution

Aarna.ml’s Digital Twin solution is a transformative tool that works with NVIDIA Air to create a comprehensive digital replica of physical network infrastructure. This enables NCPs to simulate their network topologies, test various deployment scenarios, and validate configurations before moving to live production, greatly reducing the risk of errors.

Key Features and Capabilities

  • Complete Network Simulation: The Digital Twin allows NCPs to specify and simulate their desired network topologies, including multi-tier switching, to ensure compliance with NVIDIA RA standards. This simulation environment supports detailed testing of both underlay and overlay configurations.
  • Automated Day 0 Configurations: The solution automates the initial setup of underlay networks, minimizing manual errors and significantly reducing the time required for validation.
  • Dynamic Tenant Overlays: Support for dynamic overlay configurations ensures that tenant-specific requirements are met, enabling seamless management of both east-west (GPU-to-GPU) and north-south (GPU-to-storage) traffic flows.
  • Simulation of Day 2 Operations: The Digital Twin simulates day 2 operations such as configuration changes, switch replacement, topology updates, GPU errors etc. This ensures that all such scenarios are tested and the deployment scripts and configurations are generated that could be used by NCPs for their production setup.

Benefits of aarna.ml Solution

  • Accelerated Deployment: Reduces the certification process from months to weeks by automating validation and configuration tasks.
  • Enhanced Reliability: Ensures comprehensive validation of network configurations to prevent errors before they reach production.
  • Efficient Day 2 Operations: Simplifies tenant-specific changes, configuration drift corrections, ongoing topology management and reduces MTTR by automating GPU related fault corrections.
  • Improved ROI: Maximizes operational efficiency, saving time and reducing the potential for costly network issues.

Summary

The aarna.ml solution, which utilizes NVIDIA Air, is an essential capability for NCPs looking to streamline their deployment processes, reduce risks, and maintain optimal performance in NVIDIA-based networking infrastructures. By automating testing and operational tasks, this solution empowers NCPs not only to validate complex deployments but also extend its usage for production set-up.

How to Engage

Engage aarna.ml for a Digital Twin Professional Service. Complete your day 0 and day 1 provisioning of GPU infrastructure within 2 weeks.

Explore Your Options: Learn more about how Aarna Networks’ Digital Twin technology can transform your network validation and management strategies. Contact us at info@aarna.ml today to integrate this innovative solution into your operations and unlock the full potential of your NVIDIA-based infrastructure.