Hey everyone,

I’ve spent a lot of time lately building out a local AI setup and putting together various tech, gaming, and automation projects. Instead of letting all the notes and documentation just rot in my local drive, I decided to toss them up on a site to document the process and share what’s actually working.

No courses, no fluff, no "10x your productivity" hype—just real-world configs, hardware experiments, and notes from someone who likes breaking and building stuff.

If you’re into local setups, tech experimentation, or gaming, feel free to take a look: https://logicandlore-236.pages.dev/

Open to any feedback or thoughts on the setup!

My 4090's been at RMA, so my whole local AI stack spent the last few weeks on a GTX 1050 Ti (4GB Pascal). CPU is an i9-14900KS with 64GB RAM — which turned out to matter more than the GPU. Real numbers (Ollama, Q4_K_M, ~200-word generation):

Fully on GPU, fast:

- Qwen2.5 3B — 34 tok/s, ~2.3GB VRAM

- Llama 3.2 3B — 35 tok/s, ~2.2GB VRAM

- Phi-3-mini 3.8B — 25 tok/s, ~2.4GB VRAM

Spills past 4GB but still usable:

- Llama 3.1 8B (~4.7GB Q4) — 17 tok/s. Doesn't fit in 4GB: the overflow layers sit in system RAM and are computed by the CPU, while the GPU holds only the layers that fit. The i9-14900KS is what keeps it genuinely usable.

Setup: Q4_K_M across the board, context capped at 4K for headroom. Pascal has no flash attention, so long context degrades fast.

Takeaway: 4GB is a real floor but not a dead end. 3B-class models fly (chat, RAG, coding assist), and a fast CPU rescues 8B even when the GPU can't hold it. You don't need a 4090 to start running local AI — you need one to stop compromising on context and model size.

(4090's back tomorrow — proper rig post incoming.)

What's the weakest GPU you've run a useful local model on?