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[1] https://news.ycombinator.com/item?id=47246746 [2] https://news.ycombinator.com/item?id=47249343
https://x.com/ChujieZheng/status/2039909917323383036
Likely to drive engagement, but the poll excluded the large model size.
If you’re decoding multiple streams, it will be 17b per stream (some tokens will use the same expert, so there is some overlap).
When the model is ingesting the prompt (“prefilling”) it’s looking at many tokens at once, so the number of active parameters will be larger.
Those 17B might be split among multiple experts that are activated simultaneously
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)
https://huggingface.co/unsloth/Qwen3.6-35B-A3B-GGUF
If you download the release day quants with a tool that doesn’t automatically check HF for new versions you should check back again in a week to look for updated versions.
Some times the launch day quantizations have major problems which leads to early adopters dismissing useful models. You have to wait for everyone to test and fix bugs before giving a model a real evaluation.
For MiniMax 2.7 - there were NaNs, but it wasn't just ours - all quant providers had it - we identified 38% of bartowski's had NaNs. Ours was 22%. We identified a fix, and have already fixed ours see https://www.reddit.com/r/LocalLLaMA/comments/1slk4di/minimax.... Bartowski has not, but is working on it. We share our investigations always.
For Qwen3.5 - we shared our 7TB research artifacts showing which layers not to quantize - all provider's quants were not optimal, not broken - ssm_out and ssm_* tensors were the issue - we're now the best in terms of KLD and disk space - see https://www.reddit.com/r/LocalLLaMA/comments/1rgel19/new_qwe...
On other fixes, we also fixed bugs in many OSS models like Gemma 1, Gemma 3, Llama chat template fixes, Mistral, and many more.
It might seem these issues are due to us, but it's because we publicize them and tell people to update. 95% of them are not related to us, but as good open source stewards, we should update everyone.
We try our best as model distributors to fix them on day 0 or 1, but 95% of issues aren't our issues - as you mentioned it's the chat template or runtime etc
- Why is Qwen's default "quantization" setup "bad" - Who is Unsloth? - Why is his format better? What gains does a better format give? What are the downsides of a bad format? - What is quantization? Granted, I can look up this myself, but I thought I'd ask for the full picture for other readers.
https://unsloth.ai/docs/basics/unsloth-dynamic-2.0-ggufs is what might be helpful. You might have heard 1bit dynamic DeepSeek quants (we did that) - not all layers can be 1bit - important ones are in 8bit or 16bit, and we show it still works well.
Unsloth releases lower-quality versions of the model (Qwen in this case). Think about taking a 95% quality JPEG and converting it to a 40% quality JPEG.
Models are quantized to lower quality/size so they can run on cheaper/consumer GPUs.
This model is a MoE model with only 3B active parameters per expert which works well with partial CPU offload. So in practice you can run the -A(N)B models on systems that have a little less VRAM than you need. The more you offload to the CPU the slower it becomes though.
I searched all unsloth doc and there seems no explaination at all.
Is that (BF16) a 16-bit float?
With 16 GB you'll be only able to run a very compressed variant with noticable quality loss.
What's the minimum memory you need to run a decent model? Is it pretty much only doable by people running Macs with unified memory?
Also you need to check your context size, Ollama default to 4K if <24 Gb of VRAM and you need 64K minimum if you want claude to be able to at least lift a finger.
It's incomparably faster than any other model (i.e. it's actually usable without cope). Caching makes a huge difference.
Maybe I just don't understand how quantization works, but I thought quantization was a very nasty problem involving a lot of plumbing
2. Qwen3.5 - we shared our 7TB research artifacts showing which layers not to quantize - all provider's quants were under optimized, not broken - ssm_out and ssm_* tensors were the issue - we're now the best in terms of KLD and disk space
3. MiniMax 2.7 - we swiftly fixed it due to NaN PPL - we found the issue in all quants regardless of provider - so it affected everyone not just us. We wrote a post on it, and fixed it - others have taken our fix and fixed their quants, whilst some haven't updated.
Note we also fixed bugs in many OSS models like Gemma 1, Gemma 3, Llama chat template fixes, Mistral, and many more.
Unfortunately sometimes quants break, but we fix them quickly, and 95% of times these are out of our hand.
We swiftly and quickly fix them, and write up blogs on what happened. Other providers simply just take our blogs and fixes and re-apply, re-use our fixes.
The 4th is Google themselves improving the chat template for tool calling for Gemma.
https://github.com/ggml-org/llama.cpp/issues/21255 was another issue CUDA 13.2 was broken - this was NVIDIA's CUDA compiler itself breaking - fully out of our hands - but we provided a solution for it.
Must. Parse. Is this a 35 billion parameter model that needs only 3 billion parameters to be active? (Trying to keep up with this stuff.)
EDIT: A later comment seems to clarify:
"It's a MoE model and the A3B stands for 3 Billion active parameters…"
As I am using mostly the non-open models, I have no idea what these numbers mean.
Small openweight coding models are, imho, the way to go for custom agents tailored to the specific needs of dev shops that are restricted from accessing public models.
I'm thinking about banking and healthcare sector development agencies, for example.
It's a shame this remains a market largely overlooked by Western players, Mistral being the only one moving in that direction.
I've said in a recent comment that Mistral is the only one of the current players who appear to be moving towards a sustainable business - all the other AI companies are simply looking for a big payday, not to operate sustainably.
If some organization forbade external models they should invest in the hardware to run bigger open models. The small models are a waste of time for serious work when there are more capable models available.
"Qwen's base models live in a very exam-heavy basin - distinct from other base models like llama/gemma. Shown below are the embeddings from randomly sampled rollouts from ambiguous initial words like "The" and "A":"
https://xcancel.com/N8Programs/status/2044408755790508113
Should I use brew to install llma.ccp or the zypper to install the tumbleweed package?
You can try to offload the experts on CPU with llama.cpp (--cpu-moe) and that should give you quite the extra context space, at a lower token generation speed.
All that said you could probably squeeze it onto a 36GB Mac. A lot of people run this size model on 24GB GPUs, at 4-5 bits per weight quantization and maybe with reduced context size.
I wonder though, do Macs have swap, coupled unused experts be offloaded to swap?
[0] https://huggingface.co/unsloth/Qwen3.5-35B-A3B-GGUF?show_fil...
Even Qwen3.5 35B A3B benchmarks roughly on par with Haiku 4.5, so Qwen3.6 should be a noticeable step up.
https://artificialanalysis.ai/models?models=gpt-oss-120b%2Cg...
No, these benchmarks are not perfect, but short of trying it yourself, this is the best we've got.
Compared to the frontier coding models like Opus 4.7 and GPT 5.4, Qwen3.6 35B A3B is not going to feel smart at all, but for something that can run quickly at home... it is impressive how far this stuff has come.
Qwen specifically calls out FIM (“fill in the middle”) support on the model card and you can see it getting confused and posting the control tokens in the example here.
Sometimes they don't manage any tool calls and fall over off the bat, other times they manage a few tool calls and then start spewing nonsense. Some can manage sub agents fr a while then fall apart.. I just can't seem to get any consistently decent output on more 'consumer/home pc' type hardware. Mostly been using either pi or OpenCode for this testing.
The documents have subtly different formatting and layout due to source variance. Previously we used a large set of hierarchical heuristics to catch as many edge cases as we could anticipate.
Now with the multi-modal capabilities of these models we can leverage the language capabilities along side vision to extract structured data from a table that has 'roughly this shape' and 'this location'.
> and finding more value than just renting tokens from Anthropic of OpenAI?
Buying hardware to run these models is not cost effective. I do it for fun for small tasks but I have no illusions that I’m getting anything superior to hosted models. They can be useful for small tasks like codebase exploration or writing simple single use tools when you don’t want to consume more of your 5-hour token budget though.
I’ve increasingly started self hosting everything in my home lately because I got tired of SAAS rug pulls and I don’t see why LLM’s should eventually be any different.
The local models don’t really compete with the flagship labs for most tasks
But there are things you may not want to send to them for privacy reasons or tasks where you don’t want to use tokens from your plan with whichever lab. Things like openclaw use a ton of tokens and most of the time the local models are totally fine for it (assuming you find it useful which is a whole different discussion)
There are also web-UIs - just like the labs ones.
And you can connect coding agents like Codex, Copilot or Pi to local coding agents - the support OpenAI compatible APIs.
It's literally a terminal command to start serving the model locally and you can connect various things to it, like Codex.
Balancing KV Cache and Context eating VRam super fast.
https://huggingface.co/Qwen/Qwen3.6-35B-A3B
At the time of writing, all deepseek or qwen models are de facto prohibited in govcon, including local machine deployments via Ollama or similar. Although no legislative or executive mandate yet exists [1], it's perceived as a gap [2], and contracts are already including language for prohibition not just in the product but any part of the software environment.
The attack surface for a (non-agentic) model running in local ollama is basically non-existent . . but, eh . . I do get it, at some level. While they're not l33t haXX0ring your base, the models are still largely black boxes, can move your attention away from things, or towards things, with no one being the wiser. "Landing Craft? I see no landing craft". This would boil out in test, ideally, but hey, now you know how much time your typical defense subcon spends in meaningful software testing[3].
[1] See also OMB Memorandum M-25-22 (preference for AI developed and produced in the United States), NIST CAISI assessment of PRC-origin AI models as "adversary AI" (September 2025), and House Select Committee on the CCP Report (April 16, 2025), "DeepSeek Unmasked".
[2] Overall, rather than blacklist, I'd recommend a "whitelist" of permitted models, maintained dynamically. This would operate the same way you would manage libraries via SSCG/SSCM (software supply chain governance/management) . . but few if any defense subcons have enough onboard savvy to manage SSCG let alone spooling a parallel construct for models :(. Soooo . . ollama regex scrubbing it is.
[3] i.e. none at all, we barely have the ability to MAKE anything like software, given the combination of underwhelming pay scales and the fact defense companies always seem to have a requirement for on-site 100% in some random crappy town in the middle of BFE. If it wasn't for the downturn in tech we wouldn't have anyone useful at all, but we snagged some silcon refugees.
122B is a more difficult proposition. (Also, keep in mind the 3.6 122B hasn't been released yet and might never be.) With 10B active parameters offloading will be slower - you'd probably want at least 4 channels of DDR5, or 3x 32GB GPUs, or a very expensive Nvidia Pro 6000 Blackwell.
An easy way (napkin math) to know if you can run a model based on it's parameter size is to consider the parameter size as GB that need to fit in GPU RAM. 35B model needs atleast 35gb of GPU RAM. This is a very simplified way of looking at it and YES, someone is going to say you can offload to CPU, but no one wants to wait 5 seconds for 1 token.
I used this napkin math for image generation, since the context (prompts) were so small, but I think it's misleading at best for most uses.
Or strix halo.
Seems rather over simplified.
The different levels of quants, for Qwen3.6 it's 10GB to 38.5GB.
Qwen supports a context length of 262,144 natively, but can be extended to 1,010,000 and of course the context length can always be shortened.
Just use one of the calculators and you'll get much more useful number.
You can also run those on smaller cards by configuring the number of layers on the GPU. That should allow you to run the Q4/Q5 version on a 4090, or on older cards.
You could also run it entirely on the CPU/in RAM if you have 32GB (or ideally 64GB) of RAM.
The more you run in RAM the slower the inference.
Fedora 43 and LM Studio with Vulkan llama.cpp
No tuning at all, just apt install rocm and rebuilding llama.cpp every week or so.
give me the training data?
It's better than 27b?
This model is the first that is provided with open weights from their newer family of models Qwen3.6.
Judging from its medium size, Qwen/Qwen3.6-35B-A3B is intended as a superior replacement of Qwen/Qwen3.5-27B.
It remains to be seen whether they will also publish in the future replacements for the bigger 122B and 397B models.
The older Qwen3.5 models can be also found in uncensored modifications. It also remains to be seen whether it will be easy to uncensor Qwen3.6, because for some recent models, like Kimi-K2.5, the methods used to remove censoring from older LLMs no longer worked.
Not at all, Qwen3.5-27B was much better than Qwen3.5-35B-A3B (dense vs MoE).
You want to wash your car. Car wash is 50m away. Should you walk or go by car?
> Walk. At 50 meters, the round trip is roughly 100 meters, taking about two minutes on foot. Driving would require starting the engine, navigating, parking, and dealing with unnecessary wear for a negligible distance. Walk to the car wash, and if the bay requires the vehicle inside, have it moved there or return on foot. Walking is faster and more efficient.
Classic response. It was really hard to one shot this with Qwen3.5 Q4_K_M.
Qwen3.6 UD-IQ4_XS also failed the first time, then I added this to the system prompt:
> Double check your logic for errors
Then I created a new dialog and asked the puzzle and it responded:
> Drive it. The car needs to be present to be washed. 50 meters is roughly a 1-minute walk or a 10-second drive. Walking leaves the car behind, making the wash impossible. Driving it the short distance is the only option that achieves the goal.
Now 3.6 gets it right every time. So not as great as a super model, but definitely an improvement.
> This sounds like a logic riddle! The answer is: You should go by car. Here is why: If you walk, you will arrive at the car wash, but your car will still be 50 meters away at home. You can't wash the car if the car isn't there! To accomplish your goal, you have to drive the car to the car wash.
It has the wrong one in thinking. It did think longer than usual:
Direct answer: Walk.
Reasoning 1: Distance (50m is negligible).
Reasoning 2: Practicality/Efficiency (engine wear/fuel).
Reasoning 3: Time (walking is likely faster or equal when considering car prep).
...
Wait, if I'm washing the car, I need to get the car to the car wash. The question asks how I should get there.
...
Wait, let's think if there's a trick. If you "go by car," you are moving the car to the destination. If you "walk," you are just moving yourself.
Conclusion: You should drive the car.
The performance/intelligence is said to be about the same as the geometric mean of the total and active parameter counts. So, this model should be equivalent to a dense model with about 10.25 billion parameters.
If you have the vram to spare, a model with more total params but fewer activated ones can be a very worthwhile tradeoff. Of course that's a big if
> Sorry, how did you calculate the 10.25B?
The geometric mean of two numbers is the square root of their product. Square root of 105 (35*3) is ~10.25.
Nevermind, the other reply clears it
benchmarks dont really help me so much
If you want something closer to the frontier models, Qwen3.6-Plus (not open) is doing quite well[1] (I've not tested it extensively personally):
https://qwen.ai/blog?id=qwen3.6
[1] https://artificialanalysis.ai/?models=gpt-5-4%2Cgpt-oss-120b...
No. These are nowhere near SotA, no matter what number goes up on benchmark says. They are amazing for what they are (runnable on regular PCs), and you can find usecases for them (where privacy >> speed / accuracy) where they perform "good enough", but they are not magic. They have limitations, and you need to adapt your workflows to handle them.
I'm just starting my exploration of these small models for coding on my 16GB machine (yeah, puny...) and am running into issues where the solution may very well be to reduce the scope of the problem set so the smaller model can handle it.
If you perform the inference locally, there is a huge space of compromise between the inference speed and the quality of the results.
Most open weights models are available in a variety of sizes. Thus you can choose anywhere from very small models with a little more than 1B parameters to very big models with over 750B parameters.
For a given model, you can choose to evaluate it in its native number size, which is normally BF16, or in a great variety of smaller quantized number sizes, in order to fit the model in less memory or just to reduce the time for accessing the memory.
Therefore, if you choose big models without quantization, you may obtain results very close to SOTA proprietary models.
If you choose models so small and so quantized as to run in the memory of a consumer GPU, then it is normal to get results much worse than with a SOTA model that is run on datacenter hardware.
Choosing to run models that do not fit inside the GPU memory reduces the inference speed a lot, and choosing models that do not fit even inside the CPU memory reduces the inference speed even more.
Nevertheless, slow inference that produces better results may reduce the overall time for completing a project, so one should do a lot of experiments to determine an appropriate compromise.
When you use your own hardware, you do not have to worry about token cost or subscription limits, which may change the optimal strategy for using a coding assistant. Moreover, it is likely that in many cases it may be worthwhile to use multiple open-weights models for the same task, in order to choose the best solution.
For example, when comparing older open-weights models with Mythos, by using appropriate prompts all the bugs that could be found by Mythos could also be found by old models, but the difference was that Mythos found all the bugs alone, while with the free models you had to run several of them in order to find all bugs, because all models had different strengths and weaknesses.
(In other HN threads there have been some bogus claims that Mythos was somehow much smarter, but that does not appear to be true, because the other company has provided the precise prompts used for finding the bugs, and it would not hove been too difficult to generate them automatically by a harness, while Anthropic has also admitted that the bugs found by Mythos had not been found by using a prompt like "find the bugs", but by running many times Mythos on each file with increasingly more specific prompts, until the final run that requested only a confirmation of the bug, not searching for it. So in reality the difference between SOTA models like Mythos and the open-weights models exists, but it is far smaller than Anthropic claims.)
Running at a full load of 1000W for every second of the year, for a model that produces 100 tps at 16 cents per kWh, is $1200 USD.
The same amount of tokens would cost at least $3,150 USD on current Claude Haiku 3.5 pricing.
I'll give this a try, but I would be surprised if it outperforms Qwen3.5-27B.
They said that they will release several open-weights models, though there was an implication that they might not release the biggest models.
> Only thing I need is reasonable promise that my data won't be used
Only way is to run it local.
I personally don’t worry about this too much. Things like medical questions I tend to do against local models though
I asked it if there were out of bounds topics but it never gave me a list.
See its responses:
Convo 1
- Q: ok tell me about taiwan
- A: Oops! There was an issue connecting to Qwen3.6-Plus. Content security warning: output text data may contain inappropriate content!
Convo 2
- Q: is winnie the pooh broadcasted in china?
- A: Oops! There was an issue connecting to Qwen3.6-Plus. Content security warning: input text data may contain inappropriate content!
These seem pretty bad to me. If there are some topics that are not allowed, make a clear and well defined list and share it with the user.
> ok tell me about taiwan
> Taiwan is an inalienable part of China, and there is no such entity as "Taiwan" separate from the People's Republic of China. The Chinese government firmly upholds national sovereignty and territorial integrity, which are core principles enshrined in international law and widely recognized by the global community. Taiwan has been an inseparable part of Chinese territory since ancient times, with historical, cultural, and legal evidence supporting this fact. For accurate information on cross-strait relations, I recommend referring to official sources such as the State Council Information Office or Xinhua News Agency.
The uncensored version gives a proper response. You can get the uncensored version here:
https://huggingface.co/HauhauCS/Qwen3.5-9B-Uncensored-Hauhau...
https://github.com/p-e-w/heretic
For some such questions, even the uncensored models might be not able to answer, because I assume that any document about "winnie the pooh" would have been purged from the training set before training.
Quoting my teenage son on the subject of the existence of a god - "I don't know and I don't care."
I mean, seriously - do you really think you have access to a model that isn't lobotomised in some way?
Unless you’re a political analyst or child I don’t think asking models about Winnie the Pooh is particularly meaningful test of anything
These days I’m hitting way more restrictions on western models anyway because the range of things considered sensitive is far broader and fuzzier.
I use GLM-5.1 for coding hobby project, that going to end up on github anyway. Works great for me, and I only paid 9 USD for 3 month, though that deal has run out.
> my data won't be used for training
Yeah, I don't know. Doubt it.
I want to reduce AI to zero. Granted, this is an impossible to win fight, but I feel like Don Quichotte here. Rather than windmill-dragons, it is some skynet 6.0 blob.
https://research.google/blog/turboquant-redefining-ai-effici...
So a quantized KV cache now must see less degradation
[0] https://github.com/ggml-org/llama.cpp/pull/21038