Follow-up, since your picker tops out around 8B: here's the engine and a model, if you want to see what a much bigger one does in a tab.
Engine: https://github.com/KikoCis/elffuss-engine — Apache-2.0, published today. It loads a standard GGUF over HTTP (range requests + CORS) and runs it on WebGPU: header and tensor table parsed up front, tensors read by range, dequantised inside the shader, and long dispatches split so the system compositor still gets to paint. Hybrid models work (attention + SSM), which is what a Qwen3.x needs — most of its blocks keep fixed-size state instead of a growing KV cache.
Model: https://huggingface.co/KikoCis/Qwen3.8-27B-GGUF — IQ2_M is the one to take. We measured where these quants stop driving tools: 2.93 bits/weight still completes 79/80 agentic tasks, 2.23 collapses to 37.5%, and the failure is not a wrong answer, it's emitting no tool call at all. The card has the table and the caveat that single runs are noisier than most of the differences in it.
You can also just try it without touching code: https://claw.elffuss.utopiaia.com, pick "Qwen3.8-27B IQ1 · motor propio". Fair warning — that build is ~7.6 GB, short context, and slow. It answers; it does not feel instant.
One boundary, stated plainly: the offline packer that prepares weights is not published, and the engine doesn't need it — plain GGUF is enough.
And the reciprocal question stands: what actually stopped you going past 8B in the tab — memory, load time, or the watchdog?