George Georgovassilis writes about information technology architecture
TL;DR: Qwen3.5 31B at 15 t/s generation (500 t/s prefill) on 8GB VRAM as a daily driver, not a proof of concept. Context & motivation My crusade to democratise affordable AI is in full swing, whether that means patching inference engines[COL] or pushing cheap hardware to its limits[XLM]. I’m glad to see developments in software … Continue reading Beating ChatGPT 5 on a laptop PC
I've found the vscode copilot error "No lowest priority node found" to be about a context overflow somewhere in the chain "inference engine" -> "LLM" -> copilot. Some/all of the workarounds you can try when that happens: Reduce/disable agent tools Increase the input window in chatLanguageModels.json, eg. { "id": "gemma-4-12b-it-qat-q4_0", "name": "gemma-4-12b-it-qat-q4_0", "url": "http://localhost:11434", "toolCalling": … Continue reading VScode copilot: No lowest priority node found
In a previous post [GM4] we ran gemma4 12B on a 16GB RAM budget; today we'll push the envelop and run deepseek-r1:14b-qwen-distill-q4_K_M on the same budget. While last time we got away by fiddling with the model definition, today we'll have to reconfigure the OS itself (in addition to fiddling with the model definition). While … Continue reading Running even larger LLMs on small hardware
Overview UI/browser automation tests can be brittle, because tests hook into implementation details of the UI which may not be relevant for actual user interaction. Visual test automation is more robust, because it uses the UI in the same way a user is supposed to do. This post explains a solution for visual browser automation. … Continue reading Visual browser automation with OmniParser
Google released Gemma 4 12B today. I'm a huge fan of the Gemma model family, they have improved with each iteration and consistently perform on par with larger models. It didn't run at first because it needs more VRAM that my laptop has, but there's a workaround. Here's a short instruction for how to run … Continue reading Running large LLMs on small hardware: Gemma 4 12B on a VRAM-constrained Radeon laptop
