Qwen 3.8 27B

Credit where it's due. Qwen 3.8 27B is only the second local model after Gemma 4 that managed to correctly reason through one of my private benchmarks. It took 5x as many tokens to do it and 12m30s with MTP enabled, but it did do it.Gemma 4 reasoned through it more implicitly, while Qwen 3.8 reasoned more explicitly. Laguna and Muse Glimmer failed hard on it, though they're useful for other tasks.The VRAM usage seems way less efficient than Gemma 4 or Glimmer though, with 32K of context taking 2.5GB of VRAM. With those, even with MTP or a DFlash model loaded, you could still fit 256k-768k of context. With Qwen 3.8 27B I can't even fit 128k if I quantize V to Q4_0. Maybe with some trial and error I can find some settings that perform well enough with a larger context window that it's still useful for longer tasks.Lots more testing to do, though I was getting some decent results out of Muse Glimmer which was more than twice as fast and supported huge context windows, managing to solve some bugs that Gemma 4 struggled with.... reply:...

Absolutely the best pelican I've seen from a model that runs on my laptop: https://tools.simonwillison.net/markdown-svg-renderer#url=ht...Bicycle is the right shape. Pelican beak is excellent. Nice background. Most importantly, the pelican has one leg on each side of the bicycle - that's very rare.(No chain on this bicycle though - in the reasoning trace it says "already chainstay... skip chain detail; maybe a small chainring.")I ran that on an M5 Max MacBook Pro using LM Studio and their 17GB GGUF: https://lmstudio.ai/models/qwen3.8It took 21 minutes(!) and used 22,276 reasoning tokens to produce 3,223 tokens of output.(For the "they're training on your benchmark now" crowd, all of that cheating didn't prevent it from spending 20 minutes thinking about the task first! You can see the reasoning trace in the link I shared.)For comparison, here's one I got from qwen3.8-2.4t-a95b on OpenRouter, which is pleasingly animated: https://tools.simonwillison.net/markdown-svg-renderer#url=ht... reply: For anyone who followed yesterday's Gemini 3.7 Flash pelican which rendered in Safari but not in Firefox or Chrome... https://news.ycombinator.com/item?id=49289112#49290012....

There's a real change (compared to 3.6) in the way it writes in thinking — it drops words like "to" and "we" in "We need to", talks generally in note form, drops the/and all over the place, avoids "for"."Need be helpful concise", "Need maybe not overdo", "Need ask!" Almost caveman.I have an (unsourced, vague) suspicion that this rather unique thinking trace pattern is actually hobbling the MTP predictions, which seem to perform poorly.Other notes: it uses the trick of repeating the prompt in the thinking trace.It also worries about hidden chain of thought appearing in the final answer. It talks about "desired oververbosity 9", which is new. A bit GPT-ish.It is being extraordinarily thorough in thinking through one of my code requests, but I don't know if the net result will be any better than the 35B MoE.I asked it to ask me clarifying questions — it did, and it offered me a list of defaults I could simply agree to.I don't think it is necessarily overthinking in the looping sense, but it is in the being exhaustive sense. I need to explore how it does with a tighter reasoning budget.I am impressed but I am definitely in Camp Please-35B-A3B-When?... reply:...

If anyone else is running this on an RTX 5090, https://github.com/Neroued/ninfer as inference engine gets me ~138 tokens/second, roughly double what I get with a naive llama.cpp setup. reply: I tried it, 5090, Debian 13, all libs present, it refused to build. Compiled it with docker, ran it. It stops every x amount of reasoning tokens. You would get the same speed in llama.cpp if you tell it to use a really tiny context and tiny reasoning budget.

Wow. This model is so good, and we have GLM 5.3 (seems great voor security related work) and Deepseek. In a few months we'll have Fable/Sol-like capabilities that are not coming from the big US companies. I feel as a programmer that that is more than enough. How wil OpenAI and Anthropic survive when frontier model intelligence becomes commoditized? reply: I stopped using Fable and Opus 5 because I literally can't understand the output. The waffle is so intense it no longer makes sense. I don't understand who is using those to get real work done.I'm using kimi-k3 on a real high level dev & analysis task at the moment (with Gemini flash for fast implementation) and it is fantastic.I think we have passed the point where frontier intelligence is commoditized.

As usual, the Jinja templates are messed up so use this [0] to reduce or turn off thinking, fix tool calling, keep a 100% KV cache hit rate, etc.[0] https://huggingface.co/froggeric/Qwen-Fixed-Chat-Templates reply: I'd love to understand this more. Are you saying the Qwen team spends their very impressive human and compute resources on publishing these amazing models and then botches the chat template with mundane bugs?Like maybe I just misunderstand what's the hard part but wouldn't you assume that people who can put together an impressive model can also write a proper jinja chat template for it?

If the benchmarks don't lie, this is getting very close to Opus 4.6 capability - which was the turning point for me for when AI was "good enough" that it became very hard to justify not using it.I'm sure there's some benchmaxxing going on, and some things you get only with a a larger model.But I'm feeling pretty confident if not by Gemma 5 than by mid 2028 we'll have local models that are almost always as good as Opus 4.6 was and in many cases far better. reply: What kind of things you only get with a larger model?

One thing a lot of people don't seem to factor when hyping Qwen is how much models like this tend to 'overthink' with seemingly endless 'second guessing'. 3.8 seems no different from what I've tried thus far.As capable as it is, it's hard to justify using it when a competing model (e.g. Gemma4:26b-a3b) can consistently achieve the same or similar response with only 1/10th as many 'thinking' tokens, achieve much higher tokens/second, and take a small fraction of the time. I suppose 'YMMV' depending on your use case.Also, I haven't used it enough yet to see if it's prone to infinite looping, but its predecessors sure were. reply: Reduce or turn off thinking:https://huggingface.co/froggeric/Qwen-Fixed-Chat-Templates

https://xcancel.com/Alibaba_Qwen/status/2088280182356611304

Image->html test for this.Original images: https://image.non.io/neonRamenDesigns.webpQwen 3.8 build: https://html.non.io/neonRamenQwen3.8-27bOverall I'm very impressed with how well this did. It's a big improvement over 3.6, and it feels on-par with some much, much larger models. I think this one is on-par with Gemini 3.7 Flash.One thing to note - the build for this on my RTX 6000 pro blackwell took a long time. Easily one of the longest builds I've done. It took around 2 hours to build the site. Obviously we'll have some quants for this soon that will accelerate things, but I was still surprised with how long it took.Comparison builds from this week:https://html.non.io/neonRamenGemini3.7https://html.non.io/neonRamenGLM5.3 (note: non-multimodal)

The unsloth Q8kxl https://huggingface.co/unsloth/Qwen3.8-27B-GGUF for some reason is looping and going crazy on the think part (I tried to search for an email to let the guys know but didn't find one)... I used the bartowski one and that one doesn't have that issue https://huggingface.co/bartowski/Qwen3.8-27B-GGUF?show_file_...that's using llama.cppllama-server \ -m ~/somePath/Qwen3.8-27B-UD-Q8_K_XL.gguf \ -np 1 --kv-unified \ -fa on --no-cache-idle-slots --reasoning-preserve \ --temp 0.2 \ --spec-type draft-mtp,ngram-mod --spec-draft-n-max 3 --spec-draft-n-min 1 \ --cache-type-k f16 --cache-type-v f16 \ --chat-template-kwargs '{"preserve_thinking": true}' \I tried playing with all the recommended parameters from the unsloth page with no luck...in one of the high fever ramblings it ended with amen... lol

People will claim it's not comparable to Opus despite it beating the score. I'm not sure I disagree, but I'm also unsure whether I care. Most new models nowadays are "good enough". I cannot complain because I'd rather spend that time improving my prompts and docs. Opus might be a _slight bit better_ at picking up vague hints, but it's also extremely expensive, and I hit the 5 hour limit way too quick.I care a lot about speed and efficiency right now. For my setup I would like to have 2-3 different model families. I've settled on GLM-5.3 (formerly Deepseek v4 pro 0813) for architecting, Deepseek V4 Pro 0813 for developing, and Gemini flash lite (any recent cheap model) for repo scouting. I'll add another one in the mix for reviewing (in this case Gemini 3.7) and that's all I need.I've tried most models except Grok.Qwen is too expensive IMO (Alibaba Cloud subscriptions are hard to come by and I'm not spending 50 euros a month for a tool, so 18 euros it is)....

I hope really badly that we'll get a new 35B A3B or similar MoE model!I also miss the Qwen 3 Coder Next, which was 80B A3B, there are quite a few use cases where a non-dense model https://xcancel.com/Alibaba_Qwen/status/2088280182356611304

I hope the bonsai team makes another 1bit quant of this model (or releases code/instructions on how to do it), using the Qwen3.6 27B on my 16GB mac mini has been wild . The 1bit quant feels like opus level… for the first couple turns. Then it has trouble eg switching from plan mode to act mode. This is mostly mitigated by starting a new session. (tbf this limitation is called out on the hf page)I saw unsloth has 1bit quants too so I might check that out, anybody have experience with those?

WOW, my first try running on my 2 3090's, it was a bit slow... but it FEELS like opus 4.5, i gave it an image and a broad overview of what I wanted it to build, and it built the whole thing from beginning to end.

Since it might be helpful to some, here's my current commandline for llama.cpp running on an RTX 4090 with my monitor moved to the iGPU to free up all of its VRAM.llama-server -m Qwen3.8-27B-IQ4_NL.gguf --mmproj mmproj-BF16.gguf -c 170000 --parallel 1 -ngl -1 --cache-type-k q8_0 --cache-type-v q8_0 -b 1024 -ub 512 --flash-attn on --no-context-shift --no-mmproj-offload --spec-type draft-mtp --spec-draft-n-max 5 --spec-default --cache-type-k-draft q4_0 --cache-type-v-draft q4_0 --threads 24 --jinja --reasoning on -fit offIdentical to the qwen3.6 config. With a prompt like "svg owl" (which can reuse quite a lot compared with creative writing or similar, so ngram-mod shines), I get about 70-80t/s like this, with a memory overclock of about 1.5GHz

Beats Opus 4.7 Max (w/ Claude Code) on DeepSWE (42.2 vs 40). Looks like Qwen's 27B models continue to pack some punch.Unsloth's GGUF quants are up: https://huggingface.co/unsloth/Qwen3.8-27B-GGUF

Unsloth Q4_K_M on a single 3090, llama.cpp "Generate an SVG of a pelican riding a bicycle" first try https://www.reddit.com/r/LocalLLaMA/comments/1voa3ch/comment...

I wonder how this practically compares with Muse Glimmer, especially quantized.I've got an RX 7900 XT (20GB of VRAM) and I can run glimmer with a full 128k context window with the draft model at 65-80 tok/s.This model, on the other hand, I get about 30 tok/s with a 30k context. Raising the context or loading the draft layers for MTP drops performance to 9-15 tok/s.So I wonder how big the "real world" delta between Glimmer and Qwen is here. I can already run 3-bit DSv4-flash at 9-15 tok/s with 100k~ context, and I suspect it would outperform 4-bit Qwen 3.8 27B here.I'll have to experiment and see if I just made a stupid mistake somewhere, but it looks like Glimmer might make more sense for the comically specific niche of "20GB VRAM".

Any tips on the best approach at running this at an M4 Max 128GB? Token throughput was a bit slow with the last 27B one (MLX), ended up using the A3B variant but if I could get this one to reasonable speed I'd much prefer it.

The $1500 Intel B70 with 32GB of VRAM can run this model at max context with good performance, btw. If you don't want to drop $5-10k for running DeepSeek this is your best budget option for local refactor/small scale dev help

Qwen3.6-27B has been the main LLM powering my little agentic stack. I have adopted the test and verify approach to any models allowed to run on my machine. When the "heretic" version drops, I will fire up the harness and test. Super excited to see how it stacks up against Qwen3.6!!!

The file "Just loads" on llama.cpp, the Unsloth https://huggingface.co/unsloth/Qwen3.8-27B-GGUF is an MTP file, I see mostly the same speed on pp and generation. There has to be something wrong with those benchmarks, I find extremely hard to believe a 27B model can work similar or exceed opus 4.6.

If the benchmarks are a real indication, we now have a local model that is runnable on a high-end personal PC that trades blows with the leading model Claude Opus 4.6 Max from half a year ago.Insane if that is the case. Downloading now!

Yeah this is the first model I have been able to run locally that actually feels useful, this is unreal I am considering cancelling my claude sub and going to just api (maybe GLM?) for really hard tasks.

For those commenting on the long reasoning, it may be interesting to know that the reasoning effort is set to xhigh by default [0]. Other possible values are medium, low and none. Flag for changing it in llama.cpp below, but note that the long reasoning seems to contribute a great deal to the quality. --chat-template-kwargs '{"preserve_thinking":true,"reasoning_effort":"medium"}' [0] https://unsloth.ai/docs/models/qwen3.8#thinking--preserve-th...

This is one of the most important model releases since most use cases don't need SOTA/FrontierIf you want Qwen3.8-27B Serving Configs for the DGX Spark vLLM NVFP4 and RTX 4090 llama.cpp GGUF I added the setups here https://x.com/ErdalToprak/status/2088299678085308761?s=20

Is there any way to turn off thinking if I'm using Ollama? In my particular case, the Ollama API (the software I want no-think for is tied to Ollama's bespoke API). If not, I'll stick to 3.6 for the time being...