Comment by Aurornis
7 hours ago
> Say you have a GPU with 20GB of VRAM. You're probably going to be able to run all the 3-bit quantizations with no problem, but which one do you choose? Unsloth offers[1] four of them: UD-IQ3_XXS, Q3_K_S, Q3_K_M, UD-Q3_K_XL
There are actually two problems with this:
First, the 3-bit quants are where the quality loss really becomes obvious. You can get it to run, but you’re not getting the quality you expected. The errors compound over longer sessions.
Second, you need room for context. If you have become familiar with the long 200K contexts you get with SOTA models, you will not be happy with the minimal context you can fit into a card with 16-20GB of RAM.
The challenge for newbies is learning to identify the difference between being able to get a model to run, and being able to run it with useful quality and context.
I found the KLD benchmark image at the bottom of https://unsloth.ai/docs/models/qwen3.6 to be very helpful when choosing a quant.
Qwen3.5 series is a little bit of an exception to the general rule here. It is incredibly kv cache size efficient. I think the max context (262k) fits in 3GB at q8 iirc. I prefer to keep the cache at full precision though.
I just tested it and have to make a correction. With llama.cpp, 262144 tokens context (Q8 cache) used 8.7 GB memory with Qwen3.6 27B. Still very impressive.
Yea, I'm also kind of jealous of Apple folks with their unified RAM. On a traditional homelab setup with gobs of system RAM and a GPU with relatively little VRAM, all that system RAM sits there useless for running LLMs.
That "traditional" setup is the recommended setup for running large MoE models, leaving shared routing layers on the GPU to the extent feasible. You can even go larger-than-system-RAM via mmap, though at a non-trivial cost in throughput.
Strix Halo is another option
qwen3.5 27b w/ 4bit quant works reasonably on a 3090.