NVIDIAScape (CVE-2025-23266) is a critical GPU-related vulnerability that allows attackers to break out of containers and gain root access. While some respond by layering in virtual machines, this blog walks through a better approach, how vNode uses container-native sandboxing to neutralize such attacks at the kernel level without sacrificing performance. Includes a step-by-step replication of the exploit, and a demo of how vNode prevents it.

Running multiple tenants on a single Kubernetes cluster can be complex and risky. In this post, Liquid Reply explores how vCluster offers a secure and cost-efficient solution by isolating workloads through lightweight virtual clusters.

Shared clusters are cost-effective, but when it comes to building and testing Kubernetes controllers, they create bottlenecks, from CRD conflicts to governance issues. This blog breaks down the trade-offs between shared, local, and dedicated clusters and introduces virtual clusters as the scalable solution for platform teams.

As AI and ML workloads scale rapidly, GPUs have become essential, and expensive resources. But most teams underutilize them. This blog dives into how GPU sharing in Kubernetes can help platform teams increase efficiency, cut costs, and better support AI infrastructure.

AI’s rapid growth is fueling a silent climate problem: idle infrastructure. This blog explores why multi-tenancy is key to scaling AI sustainably and how vCluster helps teams reduce waste while moving faster.

Leftover Kubernetes resources from CI jobs can drive up cloud costs and clutter your clusters. This guide shows how to automate cleanup tasks using vCluster, helping you maintain cleaner, faster CI/CD pipelines.

Managing AI workloads on bare metal Kubernetes with GPUs presents unique challenges, from weak namespace isolation to underutilized resources and operational overhead. This blog explores the pitfalls of namespace-based multi-tenancy, why running a separate cluster per team is expensive, and how vCluster enables secure, efficient, and autonomous GPU sharing for AI teams.

Running AI or ML workloads on Kubernetes? This tutorial walks you through setting up a GPU enabled GKE cluster, from configuring GPU quotas and node pools to testing workloads and optimizing for multi-team GPU usage with vCluster.

Spot instances offer massive savings but come with unpredictability. In this step-by-step guide, learn how to combine them with vCluster to build resilient, cost-effective Kubernetes environments for CI/CD, AI/ML, and more.

Kubernetes 1.33, codenamed "Octarine: The Color of Magic", lands with 64 feature updates. This blog dives into the most impactful ones and shows how to try them immediately with vCluster. From in-place pod resizing to ClusterTrustBundle, here’s what’s new.

Discover why enterprises in 2025 are shifting from traditional VMware based virtual machines to modern, Kubernetes-native architectures. Learn how adopting Kubernetes closer to bare metal simplifies infrastructure, reduces costs, and enhances scalability and efficiency.

Introduction With the release of vCluster 0.22, managing TLS certificates for virtual clusters has become much easier with the integration of cert-manager. This new built in integration allows you to issue and manage certificates within virtual clusters while leveraging cert-mana...