https://qwen.ai/blog?id=qwen3.8
The model weights will be open-sourced on Hugging Face and ModelScope next week — stay tuned.
I’m hoping they also release a new 35b a3b, for us VRAM poors, a new 9b would also be great!
Given they have not released 27b or any smaller model with Qwen3.7, I’m not holding my breath for 3.8. They also haven’t said anything public about it.
OP’s image is literally them saying something public about it
No, they said it for 3.7 too, but then never released it, that’s what I meant
i have a 6700xt with 12gb vram. i need a usable model. 9b is barly usable because of token speed. But non the less i’m exited
Save us PrismML, you’re our only hope!
The setup described in this Codacus video is what got me started getting over 100k context with decent speeds on my RTX 3070 that I’ve been using daily for the last month: https://m.youtube.com/watch?v=0AqpaFm11oI.
The TheTom fork of llama.cpp adds asymmetric TurboQuant support that allows k at tq4 and v at tq2, which allows squeezing in more KV cache without quality loss. The REAP version of the MoE model also works just fine while further reducing the model size.
The TheTom fork doesn’t have pre-built container images, so it’s necessary to build your own (the ROCm Dockerfile in the devops directory).
If anyone does want to use the fork, I just recently built the ROCm image myself and ran into an issue where the fork’s Dockerfile was pulling a UI build package from Huggingface that doesn’t exist anymore, so I swapped in the latest ROCm Dockerfile from upstream that now builds the UI from source, and that worked fine.
With which quantization ? I have a 16GB GPU, running Qwen3.5 9b UD_Q8_K_XL basically max out the VRAM usage. Maybe a MOE model fits you. Gemma 4 e4b is one with a total 8b.
And what is the token speed you are getting ?
I’ve been hoping they would release a refreshed 27b model. 3.6-27b has been my daily workhorse for personal projects.
It has indeed been really good, so much better than similarly sized models a few years ago. Would be interesting to know how far it is from what is possible to fit into a 27b model.
I’m using Gemma4:26b as of right now. Only using it as a conversation agent in home assistant with MCP tool usage.
Has anyone compared gemma and qwen in combination with home assistant and has an opinion?
I’ve found Qwen 27b better adheres to rules and tool calls, but that depends on lots of things.
One that a bit of a dark horse is Arcee Trinity Mini (26B-A3B)
https://huggingface.co/arcee-ai/Trinity-Mini-GGUF
On paper it’s not in the same bookclub as the others (unless that bookclub involved eating books) but it is obedient with tool calls IMHE
Gemma is probably good for that, as long as it’s consistently succeeding at the tool calls.
