Discussion

💜Qwen3.5 - How to Run Locally Guide

Curiositry: Qwen3.5 9b seems to be fairly competent at OCR and text formatting cleanup running in llama.cpp on CPU, albeit slow. However, I have compiled it umpteen ways and still haven't gotten GPU offloading working properly (which I had with Ollama), on an old 1650 Ti with 4GB VRAM (it tries to allocate too much memory).

acters: I have a 1660ti and the cachyos + aur/llama.cpp-cuda package is working fine for me. With about 5.3 GB of usable memory, I find that the 35B model is by far the most capable one that performs just as fast as the 4B model that fits entirely on my GPU. I did try the 9B model and was surprisingly capable. However 35B still better in some of my own anecdotal test cases. Very happy with the improvement. However, I notice that qwen 3.5 is about half the speed of qwen 3

moqizhengz: Running 3.5 9B on my ASUS 5070ti 16G with lm studio gives a stable ~100 tok/s. This outperforms the majority of online llm services and the actual quality of output matches the benchmark. This model is really something, first time ever having usable model on consumer-grade hardware.

throwdbaaway: There are Qwen3.5 27B quants in the range of 4 bits per weight, which fits into 16G of VRAM. The quality is comparable to Sonnet 4.0 from summer 2025. Inference speed is very good with ik_llama.cpp, and still decent with mainline llama.cpp.

lukan: What exact model are you using?I have a 16GB GPU as well, but have never run a local model so far. According to the table in the article, 9B and 8-bit -> 13 GB and 27B and 3-bit seem to fit inside the memory. Or is there more space required for context etc?