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6 Open Source Tools to Monitor MySQL Performance in Linux
The post 6 Open Source Tools to Monitor MySQL Performance in Linux first appeared on Tecmint: Linux Howtos, Tutorials & Guides .
MySQL ships with decent built-in diagnostics, but when your database is slowing down under load, you need command-line tools that
The post 6 Open Source Tools to Monitor MySQL Performance in Linux first appeared on Tecmint: Linux Howtos, Tutorials & Guides.Older AMD GPUs Score Another Open-Source Driver Win From Valve: DRM Format Modifiers
Timur Kristóf of Valve's Linux open-source graphics driver team isn't done driving new improvements to aging AMD GCN 1.0/1.1 era graphics cards on Linux. Beyond enhancing display support for older APUs, transitioning GCN 1.0/1 GPUs from the legacy Radeon driver to modern AMDGPU driver, and a host of other fixes and optimizations for these old GPUs going back to the Radeon HD 7000 series, he has another notable addition that was announced today. These original GCN graphics cards with pending patches to the AMDGPU kernel driver and Mesa user-space can now allow for DRM format modifiers...
Arm Mali G1 Pro Now Working With Open-Source PanVK & Panfrost Drivers
The PanVK Vulkan driver and Panfrost Gallium3D driver for Arm Mali graphics hardware is now supporting the latest "v14" hardware GPU hardware with the Arm Mali G1-Pro now being advertised as supported...
Scaling enterprise AI: Delivering Models-as-a-Service with Red Hat OpenShift AI 3.4
Discover how Red Hat OpenShift AI 3.4 and Red Hat Connectivity Link deliver Models-as-a-Service (MaaS) to centrally govern and scale enterprise AI model serving.Many enterprises have moved past the AI pilot phase. Models are running in production and teams are consuming them, but now they're hitting the governance wall. Who controls which team can access which model? Who approved that inference endpoint for customer-facing use? What does the organization owe to a compliance team asking for usage reports?The answer depends on your starting point. Many organizations are building their AI inferen
Save the date: Red Hat Summit 2027 is coming to Boston
We’re already looking forward to the next milestone in open source collaboration. For 2027, Red Hat Summit is returning to its roots in the heart of the East Coast’s tech corridor—Boston, MA. We invite you to clear your schedule for Red Hat Summit 2027, taking place May 25-27, 2027.Our annual gathering serves as the global focal point for the open source community. We look forward to welcoming thousands of customers, partners, and industry pioneers for a week dedicated to pushing the boundaries of what’s possible through collaboration and shared code.Red Hat Summit 2027 is the premier
Introducing AutoML and AutoRAG: Guided experience for AI engineers in Red Hat OpenShift AI
As organizations move from AI experimentation toward scalable production, the complexity of manually building high-performing AI models and retrieval systems often creates a significant bottleneck. Red Hat OpenShift AI 3.4 helps address this with the introduction of AutoML and AutoRAG, now available as technology previews. These tools are designed to automate the labor-intensive aspects of the AI lifecycle, allowing teams to focus on business impact rather than manual configuration.What is AutoML?AutoML in Red Hat OpenShift AI 3.4 brings enterprise-grade automated machine learning to your fing
Faster, cheaper, just as smart: Improving the economics of LLM inference with speculative decoding
Modern large language models (LLMs) are defined by their scale. GPT-3 introduced 175 billion parameters in 2020, and today, production-grade models routinely operate in the hundreds of billions, with some architectures exceeding one trillion. Each parameter represents a learned weight, collectively encoding the language, reasoning, and knowledge that make these systems capable.This scale is not incidental. Empirical research has consistently demonstrated that model capability scales predictably with size—larger models exhibit stronger reasoning, broader factual recall, and greater generaliza
Agentic AI demands a new infrastructure stack: AMD and Red Hat deliver
Agentic AI doesn’t just move AI forward, it flips the infrastructure built for traditional inference on its head. Agentic AI, systems that reason, plan, use tools, and execute multistep tasks autonomously, is rapidly moving from research into production. This shift is not just about more compute, it fundamentally changes how infrastructure must perform, scale, and be optimized for continuous, multistage reasoning workflows.But agents are fundamentally different workloads—they call models repeatedly, fan out across tools and data sources, and need to run continuously and cost-effectively. T