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Half a year ago, I wanted to see for myself what can we currently have with local LLMs. I went down the rabbit hole, learned quite a lot in the process, and shared my results in an article. The results were pretty discouraging: even with 32 GB VRAM, the best models I could run were both too slow and
It's actually good enough for simple management tasks
My smart home camera alerts used to be useless, but now they tell me what actually happened
I gave GLM-4.5-Air (106B, open weights) 12 coding tasks through opencode on my RTX 3090. It scored 0% — never edited a single file. Same model, same GPU, same tasks, but driven by a ~150-line LangGraph agent instead: 93%. The model was never the problem. The orchestrator was. Here's the benchmark —
It ain't no match for a dedicated GPU, but you can run some light LLMs on the N100
Stop Pasting Your Code Into ChatGPT For Debugging—Run LLMs Locally Instead Here's the scenario: You've got a nasty bug, and your first instinct is to copy the suspicious function into ChatGPT. Works great. Except now you've just sent your company's code, your API keys (if you weren't careful), and p
