This is just one model in the Qwen 3.6 series. They will most likely release the other small sizes (not much sense in keeping them proprietary) and perhaps their 122A10B size also, but the flagship 397A17B size seems to have been excluded.
122b would be awesome. It is the largest size you can kinda run with a beefy consumer PC. I wondered about gemma stopping in the 30b category, it is already very strong. 122b might have been too close to being really useful.
Maybe for LLMs since everyone has their own competing LLM, but with Video models, Wan 2.2 did a rug pull, left a huge gap for the community that built around Wan 2.2 too, and I don't think a single open video model has come close since. Wan is at 2.7 now, and its been nearly a year since the last update.
That's not how it works. Many people get confused by the “expert” naming, when in reality the key part of the original name “sparse mixture of experts” is sparse.
Experts are just chunks of each layers MLP that are only partially activated by each token, there are thousands of “experts” in such a model (for Qwen3-30BA3, it was 48 layers x 128 “experts” per layer with only 8 active at each token)
The point is that open weights turns puts inference on the open market, so if your model is actually good and providers want to serve it, it will drive costs down and inference speeds up. Like Cerebras running Qwen 3 235B Instruct at 1.4k tps for cheaper than Claude Haiku (let that tps number sink in for a second. For reference, Claude Opus runs ~30-40 tps, Claude Haiku at ~60. Several orders of magnitude difference). As a company developing models, it means you can't easily capture the inference margins even though I believe you get a small kickback from the providers.
So I understand why they wouldn't want to go open weight, but on the other hand, open weight wins you popularity/sentiment if the model is any good, researchers (both academic and other labs) working on your stuff, etc etc. Local-first usage is only part of the story here. My guess is Qwen 3.5 was successful enough that now they want to start reaping the profits. Unfortunately most of Qwen 3.5's success is because it's heavily (and successfully!) optimized for extremely long-context usage on heavily constrained VRAM (i.e. local) systems, as a result of its DeltaNet attention layers.
This is like saying that Open source is not important because I don't have a machine to run it on right now. Of course it is important. We don't have any state of the art Language models that are open source, but some are still Open Weight. Better than nothing, and the only way to secure some type of privacy and control over own AI use. It is my goal to run these large models locally eventually; if they all go away that is not even a possibility. . .
I can (barely, but sustainably) run Q3.5 397B on my Mac Studio with 256GB unified. It cost $10,000 but that's well within reach for most people who are here, I expect.
It would be plenty in-budget if the software part of local AI was a bit more full-featured than it is at present. I want stuff like SSD offload for cold expert weights and/or for saved/cached KV-context, dynamic context sizing, NPU use for prefill, distributed inference over the network, etc. etc. to all be things that just work for most users, without them having to set anything up in an overly error-prone way. The system should not just explode when someone tries to run something slightly larger; it should undergo graceful degradation and let them figure out where the reasonable limits are.
But it's well within the budget of a small company that wants to run a model locally. There are plenty of reasons to run one locally even if it's not state of the art, such as for privacy, being able to do unlimited local experiments, or refining it to solve niche problems.
There are way too many good uses of these models for local that I fully expect a standard workstation 10 years from now to start at 128GB of RAM and have at least a workstation inference device.
or if you believe a lot of HN crowd we are in AI bubble and in 10 years inference will be dirt cheap when all of this crashes and we have all this hardware in data centers and it won't make any sense to run monster workstations at home (I work 128GB M4 but not run inference, just too many electron apps running at the same time...) :)
> I work 128GB M4 but not run inference, just too many electron apps running at the same time.
This is somewhat depressing - needing a couple of thousand bucks worth of ram just to run your chat app and code/text editor and API doco tool and forum app and notetaking app all at the same time...
Crucial (Micron) sold 128GB of DDR5-5600 in SODIMM form for $280 a year ago. It would be slower tham the same amount on an M4 Mac, but still, I object to characterizing either as “a couple thousand bucks worth”.
Inference will be dirt cheap for things like coding but you'll want much more compute for architectural planning, personal assistants with persistent real time "thinking / memory", as well as real time multimedia. I could put 10 M4s to work right now and it won't be enough for what I've been cooking.
Just have to reclassify it as non-frivolous then. $10k's not a lot for something as important as a car, if you live somewhere where one is required. Housing is typically gonna cost you more than $10k to own. I probably spend close to $10k for food for 1.5 years.
So if you just huff enough of the AI Kool aid, you too can own a Mac Studio. Or an M5 MacBook. Or a dual 3090 rig.
For some reason you were being downvoted but I enjoy hearing how people are running open weights models at home (NOT in the cloud), and what kind of hardware they need, even if it's out of my price range.
According to this blog (https://kaitchup.substack.com/p/lessons-from-gguf-evaluation...) the UD_IQ2_M quants are quite strong (rel. error to the base is very low), so it's around 120GB of RAM needed, while the experts can be loaded into VRAM and the rest offloaded into system RAM. It's a high end consumer PC, sure, but not unaffordable.
For example, I got an older rig with a RTX 6000 ADA (48GB VRAM), 128 GB RAM and a Threadripper, which runs this quant offloaded at 20 tps
I’ve mentioned this as an option in other discussions, but if you don’t care that much about tok/sec, 4x Xeon E7-8890 v4s with 1TB of DDR3 in a supermicro X10QBi will run a 397b model for <$2k (probably closer to $1500). Power use is pretty high per token but the entry price cannot be beat.
Full (non-quantized, non-distilled) DeepSeek runs at 1-2 tok/sec. A model half the size would probably be a little faster. This is also only with the basic NUMA functionality that was in llama.cpp a few months ago, I know they’ve added more interesting distribution mechanisms recently that I haven’t had a chance to test yet.
It doesn't matter how many can run it now, it's about freedom. Having a large open weights model available allows you to do things you can't do with closed models.
Yeah I think there’s benefits to third-party providers being able to run the large models and have stronger guarantees about ZDR and knowing where they are hosted! So Open Weights for even the large models we can’t personally serve on our laptops is still useful.
If you're running it from OpenRouter, you might as well use Qwen3.6 Plus. You don't need to be picky about a particular model size of 3.6. If you just want the 397b version to save money, just pick a cheaper model like M2.7.
I think you have that backwards. Agentic coding is way more demanding than simple chat. The request/response loops (tool calling) are much tighter and more numerous, and the context is waaaaay bigger in general.
In processing power, but chat is interactive. Agentic coding, you come up with a plan and sign off on it, and then just let it go for a while. It's the difference between speed and latency.
[1] https://news.ycombinator.com/item?id=47246746 [2] https://news.ycombinator.com/item?id=47249343