Everyone keeps saying you need expensive dedicated hardware for local agents. I have an RTX 4060 Ti with 8 GB and 64 GB of system RAM, and I wanted to see how far a normal gaming PC gets if you stop running defaults. So I let Claude (Opus 5.5) go through the whole setup, change one thing at a time and measure. Same card, same models, only the config changed: Model Quant Context Download defaults Tuned (Windows) Tuned (headless Linux) Qwen3.6-35B-A3B Q4_K_XL 131k ~25 tok/s 39-45 tok/s 52-65 tok/s Qwen3.8-Flash-Next 125B iQ4_XS 131k ~4 tok/s 9-10 tok/s 17-19 tok/s Ternary Bonsai 27B PTQ1_0 64k ~4 tok/s 36 tok/s 36 tok/s Bonsai is the odd one out: it fits fully in VRAM, so there's nothing to offload and no defaults to beat. It's just the fast option for small, well scoped tasks. What actually moved the needle: Experts in system RAM, everything else in VRAM. Layer-wise offload is far worse for MoE. Dense models are bad, couldn't optimize Qwen-3.8 27B over 6 tok/s, Flash-Next is better anyways. Take the display off the GPU. A desktop eats 0.5-1.2 GB of VRAM plus GPU time, and moving it to the iGPU was worth 20-30%. Native Linux over Windows (WSL2): another 33-38% on the same hardware. llama.cpp pinned per model family. The wrong tree made VRAM thrash. KV cache quant and MTP tuned per profile. None of this needs expensive hardware. A consumer GPU plus a machine that does nothing but inference gets you most of the way, and the models now run comfortably below their listed system requirements. Every non-default setting in the repo is there because something failed on real hardware first. I also tried an RX 570 8 GB over Vulkan. If you have another 8 GB card, I'd like to see your numbers. Repo, one install script (Linux or WSL2): https://github.com/voxlo-dev/qwen-agent-8gb   submitted by   /u/ExxploreCraft [link]   [comments]