Deploying and operating an open-source Large Language Model (LLM) requires careful planning when selecting the right GPU model and memory capacity. Choosing the optimal configuration is crucial for performance, cost efficiency, and scalability. However, this process comes with several challenges.
In this blog, we will describe the factors that you need to consider to select the optimal GPU model for your LLM. We have also published a table capturing optimal GPU models to deploy and use Top-10 open source LLMs.
A few weeks back, Tiago Reichert from AWS published a very interesting blog on AWS Community showcasing how you can deploy and use the DeepSeek-R1 LLM on an Amazon EKS Cluster operating in Auto Mode. Detailed step-by-step instructions for this are documented in this Git Repo.
In this blog, we will describe how we took AWS's excellent blog and packaged it to provide a turnkey, 1-click self-service experience for non AWS administrator type users in a typical enterprise. It took one of our solution architects 30 minutes to wrap AWS's example code using Rafay's Environment Manager and PaaS.
Over the last few weeks, we have been asked to demonstrate this every day to several customers and partners. Given the significant interest in DeepSeek and the self service experience, we believe others will benefit from this blog.
In our first blog about Hubble for Cilium, we reviewed a real life example highlighting where traditional monitoring tools fall short. We then looked at how Hubble + Cilium can address these gaps. In the second blog, we discussed how Rafay provides our customers with a a tight, turnkey integration with Cilium for various cluster types (i.e. Rafay MKS for Data Centers and Public Cloud Distributions such as Amazon EKS).
In this get started guide, we will review how a platform engineer can configure, deploy and use Hubble for Cilium on a Rafay MKS Kubernetes cluster operating in a data center (aka on-premises environment). The three high level steps are:
Provision an Upstream Kubernetes Cluster in your data center using Rafay MKS
Configure and Deploy Cilium CNI as a software add-on in a Cluster Blueprint (i.e. Bring Your Own CNI)
In the first blog, we discussed how organizations can use Hubble for Cilium for observability. In this blog, we will look at how the Rafay Platform provides a tight, turnkey integration with Cilium making life easy for platform teams. In the next blog, my colleague will describe and showcase how an administrator can configure and enable Hubble on a Rafay MKS based Kubernetes cluster with the Cilium CNI.
Networking observability in Kubernetes environments is essential for troubleshooting, security, and performance optimization. Hubble, an observability platform for the Cilium CNI, addresses this challenge by providing real-time insights into network traffic, security policies, and application-layer interactions. Hubble is built on eBPF (Extended Berkeley Packet Filter) and provides deep visibility into packet flows, service-to-service communication, and security enforcement without requiring intrusive packet mirroring or modifications to application code. In a nutshell, Hubble is a fully distributed networking and security observability platform for cloud native workloads.
In this introductory blog about Hubble for Cilium, We will start with a real life example highlighting where traditional monitoring tools fall short. We will then look at how Hubble + Cilium can address these gaps. In the second blog, I will describe how Rafay provides our customers with a a tight, turnkey integration with Cilium for various cluster types (i.e. Rafay MKS for Data Centers and Public Cloud Distributions such as Amazon EKS).
As Kubernetes adoption grows rapidly in enterprises, protecting cluster data is critical. Backups ensure business continuity in case of failures, accidental deletions, or security breaches. For over 2 years, users have depended on the integrated backup/restore capability in the Rafay Platform to dramatically simplify Kubernetes backup and restore operations. When the backups artifacts are stored in public cloud environments, organizations may have a concern with security. One of the most effective ways to secure these backups is by using Server-Side Encryption (SSE). SSE encrypts data at rest within cloud storage services, protecting it from unauthorized access while minimizing operational overhead.
In this blog, I describe the value of SSE encryption for Kubernetes backups and how it enhances security and compliance. I will also describe how administrators can configure and use SSE for backups in the Rafay Platform.
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Learn about the integrated Backup/Restore capabilities in the Rafay Platform.
This blog is Part 3 of our series on Flatcar Linux and Kubernetes
In Part 1, we introduced Flatcar Linux and why it is a great fit for Kubernetes.
In Part 2, we covered how to install a Flatcar instance locally.
In this Part 3, we focus on deploying and managing Upstream Kubernetes on Flatcar Linux using Rafay MKS.
Our upcoming February release will introduce a number of new features and enhancements.We will write about these in separate blogs. This blog is focused on support for Upstream Kubernetes based on Rafay MKS on nodes running Flatcar Linux. The Rafay platform enables users to seamlessly provision new clusters and perform in-place upgrades of Kubernetes clusters, simplifying lifecycle management.
In the fast-evolving landscape of containerized applications and cloud-native technologies, choosing the right operating system for your Kubernetes cluster can sometimes make a very big difference. Enter Flatcar Container Linux, an open-source, minimal, and immutable Linux distribution tailored specifically for running containers.
Flatcar is an excellent choice for Kubernetes and modern cloud-native environments. In Aug 2024, Flatcar Linux was accepted as a CNCF project.
This is a 3-part blog series. In this blog, we'll explore what Flatcar Linux is, why it’s uniquely suited for Kubernetes, and the benefits it brings relative to generic Linux.
As part of our January release, alongside other enhancements and features, we are adding support for Kubernetes v1.32 with Rafay MKS (i.e., upstream Kubernetes for bare metal and VM-based environments).
Both new cluster provisioning and in-place upgrades of existing clusters are supported. As with most Kubernetes releases, v1.32 deprecates and removes several features. To ensure zero impact to our customers, we have validated every feature of the Rafay Kubernetes Operations Platform on this Kubernetes version.
Part 2: Considerations: Understand the key considerations before Configuring EKS Auto Mode.
In this post, we will dive into the steps required to build and manage an Amazon EKS cluster with Auto Mode template using the Rafay Platform. This exercise is specifically well suited for platform teams interested in providing their end users with a controlled self-service experience with centralized governance.
