upvote

points

by walrus0116 hours ago |
comments
upvoteby satvikpendem15 hours ago|
next
[-]
Qwen 3.6 27B dense is much better than the 35B MoE model for coding, not sure if you've tried that yet.
reply
upvoteby sheeshkebab2 hours ago|
parent|
next
[-]
27b is slow as molasses vs 35b on local stuff I have (m5 max). Mtp doesn’t make any difference either.
reply
upvoteby walrus0115 hours ago|
parent|
prev|
[-]
yes, I have, I use both. 27B slower in tok/s due to density, obviously, 35B-A3B for speed on simpler tasks.
reply
upvoteby intothemild7 hours ago|
parent|
[-]
You should enable MTP now that its available.

LLamaCPP has had some massive updates in the last week or so.

reply
upvoteby npodbielski3 hours ago|
parent|
[-]
Yes, Qwen 3.6 MoE is hitting like 80-90tk/s on Strix halo. On R9700 I had like 170t/s. It was not possible to keep up. But MoE is circling very often. I switch then to dense model and have 20-30t/s but it is able to solve quite a lot of tasks.
reply
upvoteby alfiedotwtf4 minutes ago|
parent|
next
[-]
For those speeds, I’m assuming Q4?
reply
upvoteby intothemild3 hours ago|
parent|
prev|
[-]
I get 50-60t/s tg on my r9700 with the dense, unsloth MTP quant UD-Q5_K_XL, K@8/V@4 256k context.

Using Vulkan backend.

``` llama-server -fa on -t 7 -ngl 999 --mlock --fit off --kv-offload --no-webui --metrics --chat-template-kwargs {"preserve_thinking": true} -b 2048 -ub 1024 -m /mnt/models/unsloth/Qwen3.6-27B-MTP-GGUF/Qwen3.6-27B-UD-Q5_K_XL.gguf --mmproj /mnt/models/unsloth/Qwen3.6-27B-MTP-GGUF/mmproj-F16.gguf -c 262144 --kv-unified -ctk q8_0 -ctv q4_0 --spec-type draft-mtp --spec-draft-n-max 3 --spec-draft-ngl 99 --alias unsloth/Qwen3.6-27B-MTP-GGUF --temp 0.60 --top-k 20 --top-p 0.95 --min-p 0.00 --presence-penalty 0.00 --repeat-penalty 1.00 ```

reply
upvoteby theanonymousone11 hours ago|
prev|
next
[-]
Have you seen the 8bit quantisation matter a lot? The "consensus" in r/LocalLlama is that up to 4 bits the loss is tolerable.
reply
upvoteby alfiedotwtf2 minutes ago|
parent|
next
[-]
It’s not a general rule, and depends highly on the model and the quantisation used. Don’t guess, Unsloth sometimes publish graphs in their tutorials showing the error rate vs file size… sometimes Q4 is great, other times I go for Q6
reply
upvoteby walrus0110 hours ago|
parent|
prev|
[-]
Absolutely. Difference in Q6 vs Q8 is not as immediately noticeable, but if I test by starting from a blank slate context and giving it the same complicated task with Q4 vs a Q8 GGUF file loaded, the difference is apparent. The Q4 will struggle or do 'stupid' things with even simple bash or python. Q4 might not be as noticeable for conversational purely text one on one interaction with an LLM, but when you dig deeper into something that's more esoteric in a training dataset than a chat conversation, absolutely a big gap there.

I think some of the folks in the local llm social media communities are using them for things like company-hosted customer service chat bots, or purely english text writing stuff where Q4 will probably not cause a problem. For more discrete technical work I stick pretty much exclusively to Q8.

reply
upvoteby theanonymousone8 hours ago|
parent|
[-]
Thanks a lot. How about Q8 vs FP16/BF16? Have you checked them too?
reply
upvoteby bradfa5 hours ago|
parent|
[-]
Q8 quant is very minimal fall off in terms of KLD against the lab 16 bit. If you have the memory for BF16 KV-cache (which is usually easier to stomach) then the Q8 is very close. But even Q8 quant model with Q8 KV-cache is very close.

Smaller quants for the model start to fall off but more importantly, smaller KV-cache quants fall off much faster so avoid less than Q8 there.

reply
upvoteby thot_experiment15 hours ago|
prev|
[-]
q6 is fine for that qwen with ctx @ q8, and the dense models of that size are solid at q4 with q8 ctx
reply