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Qwen3.6-35B-A3B: Agentic coding power, now open to all
431–440 of 563 posts
Re: Qwen3.6-35B-A3B: Agentic coding power, now open to all
#432Earlier quoted context omitted.
taps the sign Unified Memory Is A Marketing Gimmeck. Industrial-Scale Inference Servers Do Not Use It.
Unified Memory is mainly how consumer hardware has enough RAM accessible by the GPU to run larger models, because otherwise the market segmentation jacks up the price substantially.
UMA removes the PCIe bottleneck and replaces it with a memory controller + bandwidth bottleneck. For most high-performance GPUs, that would be a direct downgrade.
Re: Qwen3.6-35B-A3B: Agentic coding power, now open to all
#433I have been using Qwen3.5-35B-A3B a lot in local testing, and it is by far the most capable model that could fit on my machine. I think quantization technology has really upped its game around these models, and there were two quants that blew me away Mudler APEX-I-Quality. then later I tried Byteshape Q3_K_S-3.40bpw Both made claims that seemed too good to be true, but I couldn't find any traces of lobotomization doi…
Which one is best?
I would say byteshape is smaller and faster, I can’t really notice a quality difference. But I haven’t used it as much as I only started using it a few days ago.
Re: Qwen3.6-35B-A3B: Agentic coding power, now open to all
#434Wowzers, we were worried Qwen was going to suffer having lost several high profile people on the team but that's a huge drop. It's better than 27b?
This is obviously a continuation training of 3.5, it's not a new model architecture but an incremental improvement.
Re: Qwen3.6-35B-A3B: Agentic coding power, now open to all
#435Nice release from the Qwen team. 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 love the idea of building competitor to open weight models but damn is this an expensive game to play
It is, but think about how advances in computing technology have made that power available over time. A Raspberry Pi is almost 5 times more powerful than the Cray-1.
Granted, these next couple of years are going to suck because of the AI Component Drought, but progress marches on and the power and price of running today's frontier models will be affordable to mere mortals in time. Obviously we've hit the wall with Moore's law and other factors but this will not always be out of reach.
Re: Qwen3.6-35B-A3B: Agentic coding power, now open to all
#436I've been running this on my laptop with the Unsloth 20.9GB GGUF in LM Studio: https://huggingface.co/unsloth/Qwen3.6-35B-A3B-GGUF/blob/mai... It drew a better pelican riding a bicycle than Opus 4.7 did! https://simonwillison.net/2026/Apr/16/qwen-beats-opus/
interesting, I just tried this very model, unsloth, Q8, so in theory more capable than Simon's Q4, and get those three "pelicans". definitely NOT opus quality. lmstudio, via Simon's llm, but not apple/mlx. Of course the same short prompt. Simon, any ideas? https://ibb.co/gFvwzf7M https://ibb.co/dYHRC3y https://ibb.co/FLc6kggm (tried here temperature 0.7 instead of pure defaults)
try Unsloth recommended settings
Thinking mode for general tasks: temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0
Thinking mode for precise coding tasks (e.g. WebDev): temperature=0.6, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0
Instruct (or non-thinking) mode for general tasks: temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0
Instruct (or non-thinking) mode for reasoning tasks: temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0
Re: Qwen3.6-35B-A3B: Agentic coding power, now open to all
#437Nice release from the Qwen team. 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 play with the small open weight models and I disagree. They are fun, but they are not in the same class as hosted models running on big hardware. 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.
Most organizations aren't going to need the wide breadth of capabilities of the frontier models. They're risk averse and LLMs are non-deterministic, so use cases are typically more tightly scoped to tasks that involve nuanced classification that small models can easily handle even if it takes a little fine-tuning on your organizations data.
Re: Qwen3.6-35B-A3B: Agentic coding power, now open to all
#438I've been running this on my laptop with the Unsloth 20.9GB GGUF in LM Studio: https://huggingface.co/unsloth/Qwen3.6-35B-A3B-GGUF/blob/mai... It drew a better pelican riding a bicycle than Opus 4.7 did! https://simonwillison.net/2026/Apr/16/qwen-beats-opus/
I've been trying the Q4_K_M version, and sometimes it gets stuck in a loop. Gemma 4 doesn’t have this issue.
perhaps increasing repitition_penalty might be helpful
Re: Qwen3.6-35B-A3B: Agentic coding power, now open to all
#439Earlier quoted context omitted.
Unified Memory is mainly how consumer hardware has enough RAM accessible by the GPU to run larger models, because otherwise the market segmentation jacks up the price substantially.
UMA removes the PCIe bottleneck and replaces it with a memory controller + bandwidth bottleneck. For most high-performance GPUs, that would be a direct downgrade.
[deleted]
Re: Qwen3.6-35B-A3B: Agentic coding power, now open to all
#440Earlier quoted context omitted.
Yes. How do they do it? Literally they must have PagerDuty set up to alert the team the second one of the labs releases anything.
Is quantization a mostly solved pipeline at this point? I thought that architectures were varied and weird enough where you can't just click a button, say "go optimize these weights", and go. I mean new models have new code that they want to operate on, right, so you'd have to analyze the code and insert the quantization at the right places, automatically, then make sure that doesn't degrade perf? Maybe I just don't…
that is true. gguf does not support any Architecture.
for the most recent example, as of April 16, 2026 (today)
Turboquant isnt still added to GGUF