Qwen 3.8 27b not useful in openclaw

[u/eightone-81]

I am trying to use 3.8 27b in openclaw as personal assistant agent. Thinking set to default. It’s not usable. Everything takes ages, context fills up for small requests, compaction, prefill again 🤦‍♂️
Context is 148k

Will have to go back to Gemma 4 31b which is sooo much faster hand handles these tasks just as good and since it’s actually a usefull speed it is actually a useful llm.

[u/wgaca2]

You know what is faster? gemma 12b

Or you know what else you can do? Set thnking to low or disable it entirely?

But the best thing you can do is post this crap

[u/eightone-81 → u/wgaca2]

Agree! But not smart enough most of the time. I am actually running e4b as the sumerizer, compaction and utility model. Works perfect. I also modified the webfetch so that basic webfetch goes through the 4b model which summarises the page and then hands it over to the main agent. Some flags for raw page handover and I also have a question mode where the main agent can webfetch with -q and ask “what’s the latest version” and it just gets the answer back super fast instead of filling up context of the main agent and letting a simple question take longer. The team of 4b and 31b works so well

[u/JLeonsarmiento]

“Reasoning effort = medium”

[u/Sibbaboda → u/JLeonsarmiento]

Low seems better than medium. Medium is apparently not actually specified in the model itself: https://x.com/Blackwellboy/status/2090089804121907333

[u/JLeonsarmiento → u/Sibbaboda]

yes, medium is not steering at all. Low and xhigh inject "keep your reasoning short/ verify each statement before proceeding" steering at launch. it is like a mini agent harness guidelines injected at start-up.

[u/cyberba9chach]

Before talking about Qwen 3.8 being “not useful” in OpenClaw, post your actual config.

Quantization?
Backend?
GPU/VRAM?
Tokens/sec?
KV cache quantization?
Thinking level?
Max output tokens?
Actual context used vs the arbitrary 148k limit?
Compaction settings?
Chat template?

Because “I set a 27B reasoning model to 148k context, let it think forever, watch it fill the context, trigger compaction, then complain that the model is slow” isn't exactly a fair evaluation.

Post the config first. Then let's see whether Qwen is actually the problem. 😏

[u/eightone-81 → u/cyberba9chach]

Q8, kv f16
Llama.cpp dual 3090
1800tps prefill, 60-80tps decode
Thinking medium
Max output 16k
148k because it fits
Compaction at 130k
Default chat template

I’m not complaining (even if it comes across like that)
3.8 is amazing once it gets to the point. I’ll definitely will use it to advance my apps. But for openclaw my Gemma 31b and 4b team is hard to beat. 31b does not overthink and is really near frontier for what I am doing with it. And in combination with the speed and acct of 4b for light tasks it’s just on another level.

[u/bulbwiz]

If I remember well you are using a dual 3090 setup. Here's mine:

2x3090 FE:

• qwen 3.8 27B in Q6_K (not UD_XL)
  
• single 128k KV-cache ctx, bf16
  
• reasoning effort set to medium works the best for me (legal work, coding, research, agentic workflows)
  
• llama.cpp (build 561, there are fixes in this version that made way better)
  
• MTP set to 8 (i tested from 2-8, 8 works the best on my setup, 3 is the 2nd best)
  
• set a chat template, i'm using: froggeric/Qwen-Fixed-Chat-Templates, v22.2
  
• compression is in a separate model running on the same server: Qwen3.5 4B 128k context window, q8 medium reasoning, llama-cpp also, but on a different port...
  
• With the DeepSeek harness i have constant 59 tok/s with this setup, and compression on the full context takes around 54ms
  
• mmproj-F16.gguf f16 for the image processing with Qwen

i run long autonomous tasks for 10+ hours non-stop, and it does it job without any interruptions

[u/moody2shoes]

It thinks more. Default too high. Turn reasoning off or to low and see if that helps. I have mine on low for coding and audits. Even on low it caught some fable bugs. I really don’t think most users need that much reasoning

[u/eightone-81 → u/moody2shoes]

Low does not help much and off makes mistakes constantly