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.
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.
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?
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).
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
I've been finding it very practical to run the 35B-A3B model on an 8GB RTX 3050, it's pretty responsive and doing a good job of the coding tasks I've thrown at it. I need to grab the freshly updated models, the older one seems to occasionally get stuck in a loop with tool use, which they suggest they've fixed.
I guess you are doing offloading to system RAM? What tokens per second do you get? I've got an old gaming laptop with a RTX 3060, sounds like it could work well as a local inference server.
Changed into a directory recently and fired up the qwen code CLI and gave it two prompts: "so what's this then?" - to which it had a good summary across stack and product, and then "think you can find something todo in the TODO?" - and while I was busy in Claude Code on another project, it neatly finished three HTML & CSS tasks - that I had been procrastinating on for weeks.
This was a qwen3-coder-next 35B model on M4 Max with 64GB which seems to be 51GB size according to ollama. Have not yet tried the variants from the TFA.
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.
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.
Qwen3.5 35B A3B is much much faster and fits if you get a 3 bit version. How fast are you getting 27B to run?
On my M3 Air w/ 24GB of memory 27B is 2 tok/s but 35B A3B is 14-22 tok/s which is actually usable.
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?
Do you point claude code to this? The orchestration seems to be very important.
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).
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
If you’re building from source, the vulkan backend is the easiest to build and use for GPU offloading.
Yes, that's what I tried first. Same issue with trying to allocate more memory than was available.
I've been finding it very practical to run the 35B-A3B model on an 8GB RTX 3050, it's pretty responsive and doing a good job of the coding tasks I've thrown at it. I need to grab the freshly updated models, the older one seems to occasionally get stuck in a loop with tool use, which they suggest they've fixed.
I guess you are doing offloading to system RAM? What tokens per second do you get? I've got an old gaming laptop with a RTX 3060, sounds like it could work well as a local inference server.
In the article, they claim up to 25t/s for the LARGEST model with a 24GB VRAM card. Need a lot of RAM obviously
Can you give an example of some coding tasks? I had no idea local was that good.
Changed into a directory recently and fired up the qwen code CLI and gave it two prompts: "so what's this then?" - to which it had a good summary across stack and product, and then "think you can find something todo in the TODO?" - and while I was busy in Claude Code on another project, it neatly finished three HTML & CSS tasks - that I had been procrastinating on for weeks.
This was a qwen3-coder-next 35B model on M4 Max with 64GB which seems to be 51GB size according to ollama. Have not yet tried the variants from the TFA.
3.5 seems to be better at coding than 3-coder-next, I’d check it out.
Which models would that be?