GPU on Support Tools

GPU Scheduling with NVIDIA Operator: Enterprise Kubernetes Implementation Guide

Sat, 25 Jul 2026 00:00:00 -0500

Master GPU scheduling in Kubernetes with NVIDIA GPU Operator for high-performance AI/ML workloads. This comprehensive guide covers GPU resource management, Multi-Instance GPU (MIG), time-slicing, scheduling strategies, and production optimization for enterprise environments.

Self-Hosted Coding Agents on Kubernetes: Running Pi, Claude Code, and Cline Against Your Own Inference Cluster

Sat, 25 Jul 2026 00:00:00 -0500

Every agentic coding session ships source code to a third party. An agent that reads forty files to answer one question has transmitted forty files, and the enterprises that care about this are discovering an uncomfortable detail: the coding agent is not the problem. Every major agent CLI in 2026 is a protocol client that accepts a custom endpoint. The model is the only part that has to be somewhere else, and open-weight models now clear 70-80% on SWE-bench Verified under MIT and Apache 2.0 licenses. The infrastructure to keep the entire loop inside a cluster boundary already exists.

GPU Cluster Management for AI Workloads with NVIDIA Operator: Complete Production Guide

Thu, 23 Jul 2026 00:00:00 -0500

Managing GPU clusters for AI workloads requires sophisticated orchestration, resource sharing, and monitoring capabilities. The NVIDIA GPU Operator provides a comprehensive solution for deploying and managing GPU-enabled Kubernetes clusters with enterprise-grade features. This comprehensive guide covers setting up production-ready GPU infrastructure with advanced multi-tenancy, monitoring, and optimization strategies.

Kubernetes v1.33 Dynamic Resource Allocation: Advanced Hardware Management for AI, ML, and HPC Workloads

Tue, 17 Jun 2025 09:00:00 -0500

Kubernetes v1.33 Dynamic Resource Allocation: Advanced Hardware Management for AI, ML, and HPC Workloads

Introduction

Kubernetes v1.33 brings significant enhancements to Dynamic Resource Allocation (DRA), a feature quietly evolving into one of the most crucial capabilities for workloads that depend on specialized hardware. Whether you’re running AI/ML training jobs, high-performance computing, or applications needing hardware acceleration, DRA’s improvements address critical gaps in how Kubernetes manages specialized resources like GPUs, FPGAs, high-performance NICs, and vendor-specific devices.

Advanced Linux Graphics and GPU Programming: Building High-Performance Compute and Rendering Applications

Mon, 28 Apr 2025 10:00:00 -0500

Advanced Linux graphics and GPU programming requires deep understanding of modern graphics APIs, parallel computing architectures, and optimization techniques. This comprehensive guide explores building high-performance applications using Vulkan, CUDA, OpenCL, and advanced rendering techniques for both graphics and general-purpose GPU computing.

Advanced Linux Graphics Programming: DRM/KMS Development and GPU Driver Architecture

Thu, 24 Apr 2025 10:00:00 -0500

Linux graphics programming requires deep understanding of the Direct Rendering Manager (DRM) subsystem, Kernel Mode Setting (KMS), and modern GPU architectures. This comprehensive guide explores advanced graphics concepts, from low-level DRM driver development to building high-performance Vulkan applications and custom display management systems.

High-Performance Computing and GPU Programming on Linux: CUDA, OpenCL, and Parallel Computing Mastery

Sun, 13 Apr 2025 10:00:00 -0500

High-performance computing (HPC) on Linux platforms requires sophisticated understanding of parallel programming paradigms, GPU architectures, and distributed computing frameworks. This comprehensive guide explores advanced HPC techniques, from CUDA and OpenCL programming to building scalable GPU clusters and optimizing computational workloads.

Advanced Linux Machine Learning Systems: Building High-Performance ML Infrastructure and Inference Engines

Sat, 12 Apr 2025 10:00:00 -0500

Advanced Linux machine learning systems require deep understanding of neural network architectures, GPU acceleration, and distributed computing. This comprehensive guide explores building custom ML frameworks, implementing efficient inference engines, GPU optimization with CUDA, and creating production-grade machine learning infrastructure.

How to Install Nvidia GPU Drivers on K3s

Tue, 15 Oct 2024 10:00:00 -0500

How to Install Nvidia GPU Drivers on K3s

If you’re running a K3s cluster and need to leverage Nvidia GPUs for AI or machine learning workloads, this guide will walk you through the installation and configuration process of Nvidia drivers for your Kubernetes environment.