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2025

Dynamic Resource Allocation for GPU Allocation on Rafay's MKS (Kubernetes 1.34)

This blog demonstrates how to leverage Dynamic Resource Allocation (DRA) for efficient GPU allocation using Multi-Instance GPU (MIG) strategy on Rafay's Managed Kubernetes Service (MKS) running Kubernetes 1.34.

In our previous blog series, we covered various aspects of Dynamic Resource Allocation (DRA) in Kubernetes:

DRA is GA in Kubernetes 1.34

With Kubernetes 1.34, Dynamic Resource Allocation (DRA) is Generally Available (GA) and enabled by default on MKS clusters. This means you can immediately start using DRA features without additional configuration.

Prerequisites

Before we begin, ensure you have:

  • A Rafay MKS cluster running Kubernetes 1.34 (see MKS v1.34 Blog)
  • GPU nodes with compatible NVIDIA GPUs (A100, H100, or similar MIG-capable GPUs)
  • Container Device Interface (CDI) enabled (automatically enabled in MKS for Kubernetes 1.34)
  • Basic understanding of Dynamic Resource Allocation concepts (covered in our previous blog series)
  • Active Rafay account with appropriate permissions to manage MKS clusters and addons

Kubernetes v1.34 for Rafay MKS

As part of our continuous effort to bring the latest Kubernetes versions to our users, support for Kubernetes v1.34 will be added soon to the Rafay Operations Platform for MKS cluster types.

Both new cluster provisioning and in-place upgrades of existing clusters are supported. As with most Kubernetes releases, this version also deprecates and removes a number of features. To ensure there is zero impact to our customers, we have made sure that every feature in the Rafay Kubernetes Operations Platform has been validated on this Kubernetes version. This will be promoted from Preview to Production in a few days and will be made available to all customers.

Kubernetes v1.34 Release

Deploy Workload using DRA ResourceClaim in Kubernetes

In the first blog in the DRA series, we introduced the concept of Dynamic Resource Allocation (DRA) that recently went GA in Kubernetes v1.34 which was released end of August 2025.

In the second blog, we installed a Kuberneres v1.34 cluster and deployed an example DRA driver on it with "simulated GPUs". In this blog, we’ll will deploy a few workloads on the DRA enabled Kubernetes cluster to understand how "Resource Claim" and "ResourceClaimTemplates" work.

Info

We have optimized the steps for users to experience this on their laptops in less than 5 minutes. The steps in this blog are optimized for macOS users.

GPU/Neo Cloud Billing using Rafay’s Usage Metering APIs

Cloud providers offering GPU or Neo Cloud services need accurate and automated mechanisms to track resource consumption. Usage data becomes the foundation for billing, showback, or chargeback models that customers expect. The Rafay Platform provides usage metering APIs that can be easily integrated into a provider’s billing system. '

In this blog, we’ll walk through how to use these APIs with a sample Python script to generate detailed usage reports.

Usage Metering

Upstream Kubernetes on RHEL 10 using Rafay

Our upcoming release update will add support for a number of new features and enhancements. This blog is focused on the upcoming support for Upstream Kubernetes on nodes based on Red Hat Enterprise Linux (RHEL) v10.0. Both new cluster provisioning and in-place upgrades of Kubernetes clusters will be supported for lifecycle management.

RHEL 9.2

Support for Parallel Execution with Rafay's Integrated GitOps Pipeline

At Rafay, we are continuously evolving our platform to deliver powerful capabilities that streamline and accelerate the software delivery lifecycle. One such enhancement is the recent update to our GitOps pipeline engine, designed to optimize execution time and flexibility — enabling a better experience for platform teams and developers alike.

Integrated Pipeline for Diverse Use Cases

Rafay provides a tightly integrated pipeline framework that supports a range of common operational use cases, including:

  • System Synchronization: Use Git as the single source of truth to orchestrate controller configurations
  • Application Deployment: Define and automate your app deployment process directly from version-controlled pipelines
  • Approval Workflows: Insert optional approval gates to control when and how specific pipeline stages are triggered, offering an added layer of governance and compliance

This comprehensive design empowers platform teams to standardize delivery patterns while still accommodating organization-specific controls and policies.

From Sequential to Parallel Execution with DAG Support

Historically, Rafay’s GitOps pipeline executed all stages sequentially, regardless of interdependencies. While effective for simpler workflows, this model imposed time constraints for more complex operations.

With our latest update, the pipeline engine now supports Directed Acyclic Graphs (DAGs) — allowing stages to execute in parallel, wherever dependencies allow.

Important Update: Changes to Bitnami Public Catalog

Recently, Bitnami announced significant changes to its container image distribution here. As part of this update, the Bitnami public catalog (docker.io/bitnami) will be permanently deleted on September 29th.

What’s Changing

  • All existing container images (including older or versioned tags such as 2.50.0, 10.6, etc.) will be moved from the public catalog (docker.io/bitnami) to a Bitnami Legacy repository (docker.io/bitnamilegacy).
  • The legacy catalog will no longer receive updates or support. It is intended only as a temporary migration solution to give users time to transition.

Simplifying Day-2 Operations with Agent Pools

Implementing Day-2 Operations such as agent replacement is cumbersome today because every configuration tied to a previous agent must be reconfigured manually. This makes tasks like scaling, retiring agents, or handling failures both error-prone and time-consuming.

To address this pain point, we are introducing the concept of an Agent Pool.

Why Agent Pools?

Instead of binding configurations directly to individual agents, customers can now attach multiple agents to a shared Agent Pool. Configurations such as Environment Templates and Resource Templates reference the pool, rather than a single agent.

This simple shift brings significant operational benefits:

  • Seamless Failover and Replacement
    Add or remove agents from a pool without reconfiguring existing associations.

  • Simplified Day-2 Operations
    Manage scaling, upgrades, and retirements without disruption.

  • Load Balancing
    Distribute load across multiple agents within a pool for higher availability and performance.

Enable Dynamic Resource Allocation (DRA) in Kubernetes

In the previous blog, we introduced the concept of Dynamic Resource Allocation (DRA) that just went GA in Kubernetes v1.34 which was released in August 2025.

In this blog post, we’ll will configure DRA on a Kubernetes 1.34 cluster.

Info

We have optimized the steps for users to experience this on their macOS or Windows laptops in less than 15 minutes. The steps in this blog are optimized for macOS users.

NVIDIA Performance Reference Architecture: An Introduction

Artificial intelligence (AI) and high-performance computing (HPC) workloads are evolving at unprecedented speed. Enterprises today require infrastructure that can scale elastically, provide consistent performance, and ensure secure multi-tenant operation. NVIDIA’s Performance Reference Architecture (PRA), built on HGX platforms with Shared NVSwitch GPU Passthrough Virtualization, delivers precisely this capability.

This is the introductory blog in a multi part series. In this blog, we explain why PRA is critical for modern enterprises and service providers, highlight the benefits of adoption, and outline the key steps required to successfully deploy and support the PRA design/architecture.