With a couple of releases back, we added EKS Auto Mode support in our platform for doing either quick configuration or custom configuration. In this blog, we will explore how you can create an EKS cluster using quick configuration and then dive deep into creating custom node classes and node pools using addons to deploy them on EKS Auto Mode enabled clusters.
In Part-1, we explored how Rafay GPU PaaS empowers developers to use fractional GPUs, allowing multiple workloads to share GPU compute efficiently. This enabled better utilization and cost control — without compromising isolation or performance.
In Part-2, we will show how you can enhance this by provide users the means to select fractional GPU memory. While fractional GPUs provide a share of the GPU’s compute cores, different workloads have dramatically different GPU memory needs. With this update, developers can now choose exactly how much GPU memory they want for their pods — bringing fine-grained control, better scheduling, and cost efficiency.
Enterprises and GPU Cloud providers are rapidly evolving toward a self-service model for developers and data scientists. They want to provide instant access to high-performance compute — especially GPUs — while keeping utilization high and costs under control.
Rafay GPU PaaS enables enterprises and GPU Clouds to achieve exactly that: developers and data scientists can spin up resources such as Developer Pods or Jupyter Notebooks backed by fractional GPUs, directly from an intuitive self-service interface.
This is Part-1 in a multi-part series on end user, self service access to Fractional GPU based AI/ML resources.
This blog is part of our DRA series, continuing from our earlier posts: Introduction to DRA, Enabling DRA with Kind, and MIG with DRA . This post focuses on pre-DRA vs post-DRA GPU management on Rafay upstream Kubernetes clusters.
In the previous blog, we learnt the basics about NIM (NVIDIA Inference Microservices). In this follow-on blog, we will do a deep dive into the NIM Kubernetes Operator, a Kubernetes-native extension that automates the deployment and management of NVIDIA’s NIM containers. By combining the strengths of Kubernetes orchestration with NVIDIA’s optimized inference stack, the NIM Operator makes it dramatically easier to deliver production-grade generative AI at scale.
Generative AI is moving from experiments to production, and the bottleneck is no longer training—it’s serving: getting high-quality model inference running reliably, efficiently, and securely across clouds, data centers, and the edge.
NVIDIA’s answer is NIM (NVIDIA Inference Microservices). NIM a set of prebuilt, performance-tuned containers that expose industry-standard APIs for popular model families (LLMs, vision, speech) and run anywhere there’s an NVIDIA GPU. Think of NIM as a “batteries-included” model-serving layer that blends TensorRT-LLM optimizations, Triton runtimes, security hardening, and OpenAI-compatible APIs into one deployable unit.
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.
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.
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.
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.
Rafay provides a tightly integrated pipeline framework that supports a range of common operational use cases, including:
This comprehensive design empowers platform teams to standardize delivery patterns while still accommodating organization-specific controls and policies.
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.