Comment by RandyOrion

Thank you Qwen team for this release.

Compared to closed weight (especially unreleased and access-limited) and open weight/source large sparse MoE LLMs/VLMs, open weight/source small dense models benefits public the most because they just reaches more people.

Compared to Qwen 3.6, 3.8's thinking style changed drastically. With xhigh budget, it thinks a lot MORE, and longer thinking session directly translates to better performance. This tradeoff between performance and computation, memory, etc. is meaningful to me.

However, because Qwen 3.6 and 3.8 share the same architecture, with 32GB vram, llama.cpp, IQ4_XS model, MTP and FP16 mmproj, I can only get 200k context, which is not good compared to 640k context of muse glimmer. Hopefully this problem will be solved in Qwen 4.0 release.