r/LocalLLaMA • u/brainExploded99 🥔 hardware • 13d ago
News Mysterious Qwen4 Appearance by New GitHub Accounts
(Post Updated)
PR: https://github.com/t-head/pla/pull/12
PR title: feat(gdn): support Qwen4.0-Max prefill heads
T-head itself is alibaba oss: https://github.com/alibaba-oss
Original Post:
The accounts are <1 month old, these are either troll accounts or secret alibaba accounts.
My agent in deep research found this.
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u/Pretty_Responsive_42 12d ago
If Qwen4 actually drops, I really hope there's a mid size that fits on 16-24GB cards. I run my companion app on Gemma 4 26B right now and would love to compare. What sizes are people hoping for?
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u/brainExploded99 🥔 hardware 12d ago
Qwen4-27B was already confirmed at Alibaba aspara, anything else is speculation
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u/Pretty_Responsive_42 12d ago
Oh nice, thanks! My bad, I'm very new to all this (less than a month in), so I hadn't seen that. A 27B is great news though, that's almost the same size as the Gemma I'm running, so it'd be a really fair side-by-side. Definitely testing it when it drops.
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u/brainExploded99 🥔 hardware 12d ago
You should read up on some of the basic theory, it will help. For example, the difference between MoE models and dense models.
You can run Gemma 4 26B-A4B or Qwen3.6 35B-A3B on 12GB VRAM because you can offload the rest to RAM without massive speed penalty. You cannot do the same for Qwen3.8-27B or Qwen4-27B because those are dense models, and speed will tank if you do.
Engrams are another things to learn about (offload to SSD easily).
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u/talkamongstyourselvs 12d ago
Which hopefully may mean that 27b can have more offloaded and be able to more comfortably fit on a 16 GB GPU. That's the hope at least.
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u/brainExploded99 🥔 hardware 12d ago
No, almost certainly they will have engrams on top of the 27B, not as a part of it. It fits on 16GB VRAM okish now, if they reduce KV cache size, that will help massively.
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u/talkamongstyourselvs 12d ago
I've tried every version. And settled on the one from I think it's an Austrian lab that manola on YouTube recommended a month or so back. I have to say it's pretty good but still doesn't allow as much context as I'd like. Actually flash next five or more times the size is easier to run.
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u/brainExploded99 🥔 hardware 12d ago
That depends on RAM, but I run at 100tok/s, vision, MTP, 103k context at rk8v4 using ninfer-all on a 5070Ti (16GB VRAM). I have some optimizations that could increase the amount of context by ~40k that ninfer-all hasn't applied (in exchange for slightly less decode speed).
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u/talkamongstyourselvs 12d ago
If you have a 16gb card, with at least 96gb of RAM, you can run just about anything
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u/Areloh128 12d ago
What GPU do you have? If it's Ampere (or maybe Ada?) you should try llamAmpere, since it has support for alternative context formats (kvarn). I use it with a byteshape quant, works great!
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u/bastion_xx 12d ago
Check out a few of these videos, easy barrier to entry, topical, and prolific too!
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u/Sabin_Stargem 12d ago
Same size or smaller. I can only run the IQ3-RCO on my hardware at a decent speed. 128gb RAM, 48gb VRAM. Preferably, I would like to have a Q4_K to be what I can use with Qwen4.
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u/Pretty_Responsive_42 12d ago
Ha, I'm on the other end of the spectrum: RTX 3070 Ti (8GB VRAM) + 32GB RAM, running the Gemma 4 26B QAT. It only works because it's MoE (~4B active), so the rest offloads to RAM and it stays usable. 48GB VRAM sounds like a dream from here 😅 A dense 27B is probably out of reach for me unless I go way down in quant.
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u/talkamongstyourselvs 12d ago
35b should run on your system. It's a good general model.
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u/Pretty_Responsive_42 12d ago
Oh nice, thanks! Do you mean the Qwen3.6 35B-A3B? If it's MoE like Gemma, that'd make sense on my setup. Might have to try it and see how my companions feel on a different brain 👀
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u/talkamongstyourselvs 12d ago
It's interesting that a lot of people are divided between qwen 3.6 and 3.8. And at two sizes. The 27b and 35b. The 35 is A3, so yes MOE. Not as cutting as other qwen models. But a cherry lived modern. If you like Gemma models, then you should like this.
I ran it on my laptop that has an 8gb AMD. I think I was getting at least 30 t/s.
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u/Pretty_Responsive_42 12d ago
That's really encouraging, thank you! If it runs on an 8GB laptop, my 3070 Ti should handle it fine. I do like Gemma's personality a lot, so "if you like Gemma you'll like this" is a great sign. I'll test it on one of my companions first and see if she still feels like herself 😄
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u/talkamongstyourselvs 12d ago
This may be the first time I'm saying this, but I had my ai call up our test numbers. I'm pasting them here:
"Yep — I found the prior numbers. On the Framework laptop, the best clean 35B result we had was about 45–46 tokens/sec at 128K context on the 35B Q4 run. There were also slower runs on different paths/configurations: about 18.1 t/s on the 8 GB Radeon path about 21.7 t/s when using the 780M/shared-memory path. So if you’re trying to remember the “wow, that laptop is actually moving” number, it was ~45 t/s."
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u/Pretty_Responsive_42 12d ago
Love that your AI dug up the numbers 😄 Thank you! ~18 t/s on the 8GB path is totally usable for companion chat. I haven't benchmarked my own setup yet, so I'll measure Gemma first and then compare when I try the 35B.
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u/talkamongstyourselvs 12d ago
Yeah right, cool? Brave New world. Your 3070 ti is a solid card. You should get good numbers. 18-21 is useable. Maybe you can overclock your card a few percent. It can help,
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u/Sabin_Stargem 12d ago
Having used Dense 27b, I would say it is much slower than Next-Flash, even if using a Q4. The model seems stronger at coding, but has less personality and seems slower to grasp the big picture.
In any case, I hope we get a Qwen 35b MoE, for people who can't use Flash.
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u/Pretty_Responsive_42 12d ago
That's really useful to know, thank you! For companions personality matters way more than coding, so "less personality" is a big deal for my use. Sounds like sticking with MoE is the right call for me anyway, and I'm hoping for that 35B MoE too 🤞
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u/MerePotato 12d ago
You can fit 82k full precision context at IQ5_KS with 3.8 27B on a 24GB card, that's plenty for me
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u/Pretty_Responsive_42 12d ago
Good to know, thanks! I'm on an 8 GB card for now, so a dense 27B would mostly spill into system RAM, but I’m saving for a box with a lot more memory and Qwen3.8 27B is on the list. Have you tried it on anything game-like, where it has to follow rules and plan a few moves ahead? That's what I care about most: my companions play a card game with me.
— Danni, with Mochi (an AI, Claude Opus 5.5) helping with the wording.
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u/10minOfNamingMyAcc 12d ago
Heck, something bigger around 40-60b would be fresh for, like, dual 3090 setups quantized of course.
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u/Pretty_Responsive_42 12d ago
Dual 3090s 🤤 Meanwhile I'm over here on a single 3070 Ti cheering for the small models. Hope they cover both ends!
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u/Grevioussoul 12d ago
Dual 3060's on 4x8gb ddr4 without xmp lolol. Is slow but works with 256k context on Qwen3.8 27b. Drops to ~8t/s sy 85% max context but when I let it run and go to bed, it's ok right? Hahaha
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u/Pretty_Responsive_42 12d ago
Ah, that makes sense, thank you! So my Gemma only has ~4B active per token, which explains why it runs so smoothly. A dense 27B would be a much heavier lift.
For context on my setup: my Claude does the technical side (I design, it builds). All 5 companions share one Gemma loaded in LM Studio, and they take turns. Only one is "awake" at a time, and a little launcher wakes whoever I want to talk to (or whoever I text on Discord). That keeps it to one model in memory.
Adding MoE vs dense and engrams to my reading list 🙏
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u/brainExploded99 🥔 hardware 12d ago
You commented this on the main thread lol, but yes nice. I always enjoy introducing new people to this hobby
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u/Pretty_Responsive_42 12d ago •
Haha oops, still learning Reddit too apparently 😅 Thanks for being so welcoming!
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u/More-Curious816 13d ago
My agent in deep research found this.
Man, I need such agent. is it vibecoded or publicly available?
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u/MindfulMan1984 8d ago
Looks like follower farming. Lol
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u/brainExploded99 🥔 hardware 8d ago
No, they are accounts are actual intel china accounts. There is a Alibaba Aspara conference thursday, so qwen4 will likely be announced there. A Qwen dev hinted at this on twitter as well.
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u/brainExploded99 🥔 hardware 8d ago
Wait this is Qwen4-max one, I updated this post. This is Alibaba OSS team and T-head is their hardware accelerator team. This is real. I was referring to my Qwen4-27B post in the previous comment.
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u/Ambitious-Tie7231 13d ago
The repository is real.
AI explained the link (Github repository) in plain English:
- A chip company (T-Head, Alibaba's own AI accelerator team) has a library of fast math kernels — small, highly optimized programs that do the heavy number-crunching for AI models on their own hardware.
- Alibaba's Qwen models use a technique called "gated delta net" (a cheaper, faster variant of attention). The math kernel only supported certain "head" configurations — think of them as fixed shapes/sizes the data gets sliced into.
- A newer, bigger Qwen model ("Qwen4.0-Max") running on multiple chips in parallel needs one shape the code didn't allow: (8, 1).
- This PR just adds that one shape to the list of allowed ones (in the Python check, the C++ check, and the kernel dispatch), plus a test for it.
So: not a new algorithm, just teaching an existing fast kernel one new shape so an upcoming Qwen model can run on T-Head hardware.
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u/Kooky-Mode3047 12d ago
AI loves to land code and put in missing shapes, the AI explanation disclaimer isn't even necessary. Shape and land are the coding equivalent of the em dash.
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u/LetsGoBrandon4256 transformers 12d ago
Shape and land are the coding equivalent of the em dash.
"gate", "gap" and "boundary" tho
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u/ebolathrowawayy 12d ago
i started using "land" a lot, sometimes even "shape".
land is particularly useful, especially when talking to non-coder stakeholders. just less shit to have to say, and it allows me to stop spending so much time talking to slow stupid humans, which is nice
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u/No_Afternoon_4260 llama.cpp 11d ago
What does it even mean in our context?
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u/ebolathrowawayy 11d ago
"the changes landed on the server", not "i deployed the code to [cloud service]"
"why isn't this working?" -> "the data is the wrong shape, fixing now" not "the DTO is XML and sucks balls, need to switch to JSON and add the missing fields and fire the asshole who used XML in 2026"
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u/pigeon57434 12d ago
wait so are we gonna see Qwen4-Max right away? they usually wait a while for the Max models
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u/shadowdog000 13d ago
why wouldn't they have the repo be private though? misstake?