Running multiple GPUs for local AI inference at stock power settings is wasteful. Consumer cards like the RTX 3090 draw 350W by default, but AI inference workloads are typically memory-bandwidth bound, not compute bound. The GPU cores spend much of their time waiting for data, burning power for no performance
The NVIDIA Tesla V100 and Tesla P40 are passively cooled cards, designed for data centre chassis with high-volume front-to-back airflow. Used in a desktop workstation or a home server they will thermal throttle within minutes, because there are no onboard fans. This article describes a Dockerised solution that reads GPU
The NVIDIA Tesla V100 has become a surprisingly attractive GPU for local LLM inference, thanks to its end-of-life status causing a flood of cheap used cards on the market. This article describes how to install it on Fedora 44, and the exact steps to get it working with Docker and
I recompiled Chromium with JavaScript, cookies, notifications, and profiles disabled - no tracking, no banners, no nonsense. Here's how.
How to run SketchUp 2025 on Linux by copying a Windows installation into Steam with GE-Proton. Early draft with working steps.