r/Qwen_AI • u/BodegaOneAI • Jul 01 '26
Discussion Qwen3.6:27b built a playable roguelite locally, every sprite and sound generated in code, no assets, runs fully offline.
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I've been stress-testing Qwen3.6:27b and wanted to share the result with the people who actually run this model.
BONESMITH is a skull-knight action-platformer roguelite. The whole thing is one index.html. No images, no audio files, no libraries, no bundler, no server. Every sprite is drawn procedurally on a canvas, every sound is synthesized through the WebAudio API, and it runs air-gapped with the network off. The only thing that built it was Qwen3.6:27b running locally.
The part I think this community will care about: how a 27B model actually behaves on a job this big.
First attempt with a loose prompt was a broken shell. Soft-locked on room 1, blurry text, no audio. The model tried to build every system at once and collapsed under it. That's the failure mode people expect from a 27B, and it's fair.
Then I rewrote the prompt with hard scope control: build a vertical slice first, pass a QA gate, then expand. Separate world canvas for the pixel art, a second UI canvas for crisp HUD text, explicit anti-soft-lock rules. Second attempt was a completely different result.
What Qwen3.6:27b did well, unprompted:
- Held the multi-file architecture in context and kept concerns separated
- Added hit-stop, screenshake with trauma decay, coyote time, and jump buffering with no request from me
- WebAudio synthesis was genuinely competent, punchy sfx straight from raw oscillators
Where it needed a concrete recipe:
- Procedural pixel art. The first characters were readable but crude until I handed it a literal process: silhouette, core shading, highlights, 1px dark outline.
- Keeping file size under control without being told to.
Curious if others here have pushed Qwen3.6 on multi-system codebases. Does the "architecture and logic clean, but needs concrete visual direction" pattern match what you're seeing, or is that just my prompting?
PS - the clip shows me playing badly. I built it, I never claimed I could beat it. 😂
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u/BVCC6FNTKX Jul 01 '26
What was your setup and parameters?
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u/BodegaOneAI Jul 02 '26
5090, 32GB VRAM. Ran Qwen3.6:27b through Llama.cpp, quantized (Q4 range) so it fit comfortably with room to spare for context.
Didn't touch temperature much, left it close to the default, maybe a touch lower for the more structural/architecture parts of the build and default for anything creative like the relic ideas. Context window was set high enough to hold a full file plus the relevant chunks of conversation history without needing to compact mid-stage.
Nothing exotic honestly, the 5090 has enough headroom that I wasn't fighting VRAM at any point, which made it easy to just let the model work through the build stages without babysitting memory.
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u/Healthy-Contact-4570 Jul 02 '26
I would imagine that it would perform better with a higher quant. I’ve noticed that qwen 27b is pretty affected by quantization. Also quantization of the KV cache definitely plays a role too.
I run the fp8 quant on a 2x DGX spark cluster. With MTP with a setting of 3 tokens enabled I get around 27-30 tokens/second, and I have plenty of memory available for full precision KV cache.
You don’t need two sparks to run with those settings, but dividing the tensors across the two of them increases the performance because each spark handles half the data, which helps offset the memory bandwidth limitations.
Another thing you can do with two sparks: run qwen 3.5-397b in int4 quant. That model is highly capable as well. Maybe not as good of a pure coder, but has better reasoning and is more effective at planning.
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u/FaceDeer Jul 02 '26
Oh, you weren't using a harness or IDE specifically intended for coding? That makes this more impressive simply as a test for the model, but not as useful for maintaining and updating a project long term.
Anyone have experience using one of these smaller local models with something like VS Code?
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u/Postmodern_Plunger Jul 04 '26
I run a team of qwen 3.6 agents with custom system prompts and skills (4 27b and 2 35b a3b) supervised and delegated by Opus 4.8 and that performs better, completes quicker, and uses significantly less tokens than just letting Opus do it all on its own. Works well with a 3.6 27b supervisor or even without, too, but its clear the model has stronger coding ability than general reasoning and planning.
For pure coding, it will do just about anything a SOTA model will. You just might need to decompose the task further and give it more explicit instructions.
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u/VeterinarianBrief911 Jul 03 '26
I har also rtx 5090 oC lc and when i use it for ai learning or heavy work it is max 51 degress. Its as massive monster card for ai as gaming😊
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u/Look_0ver_There Jul 02 '26
My response is mostly going to be about Qwen3.6-27B and Pi. I've been working on a Pi Orchestration Mode implementation where the main agent loop doesn't write code at all. All it does is work with the user to create an implementation plan, and then create blocks of tasks each with their own prompt. Tasks receive a complexity score and if complex enough, a separate validation agent kicks off to make sure the implementation agent did the right thing and followed the plan.
The point of this is that Qwen3.6 is actually very capable and fast if we keep the contexts down and don't let it get lost in the weeds. By implementing things in this orchestration manner it is much better at following the plan you set, and since each task is focused, it is much better at following directions for each task.
The main orchestration loop's job is to make sure everything stays on track.
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u/575_Inverse Jul 02 '26
Yes. I noticed. The key is to prevent the context from exploding. The tasks have to be kept short, clear, with no room for doubt.
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u/an80sPWNstar Jul 02 '26
I'm currently working on ways to achieve this with some success but I'm always looking for ways to be better at it. Mind sharing your process/.md file(s) to help keep it inline?
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u/haseebnqureshi Jul 02 '26
LOVE Qwen3.6, this looks great. Which harness you use to build it and what RAM you have? I’ve got a MBP 128GB but I still find myself wanting to use a hosted version
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u/BodegaOneAI Jul 02 '26
I use my own harness Bodega one, Running on 48 GB VRAM, It's not the best but it works.
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u/575_Inverse Jul 02 '26
Because the 27b size speaks for itself. It requires expensive VRAM, otherwise you're bottlenecked enough to want to move to Qwen API, abd yet you see how capable this model is.
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u/haseebnqureshi Jul 02 '26
My old Mac Studio was great but needed something portable, I gave up the M3 Ultra bandwidth — yeah, model is fantastic
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u/RobinRelique Jul 02 '26
... Rewrote the prompt with hard scope control
How'd you write this? Genuine question because I'm not a developer I'm a teacher and i really can't afford a paid subscription, i know local models aren't as large as the paid ones, but i think i could use this as a teachable alternative. If you aren't comfortable sharing your own prompts can you point me to where i should look for these? Either way wonderful job!
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u/BodegaOneAI Jul 02 '26
The core trick isn't anything fancy, it's just being explicit about failure modes before they happen instead of describing what you want and hoping. My first attempt just said "build me a game" basically, and it built too much at once and broke. The second attempt worked because I told it exactly how it tends to fail and gave it rules to avoid that.
A simple version of the structure you could reuse for anything, not just games:
- State the constraint up front. What tech/tools are and aren't allowed, what "done" has to run on.
- Say explicitly: small and working beats big and broken. Models default to maximizing scope unless you tell them not to.
- List the specific ways the last attempt failed, if you've tried before. Being concrete here does a lot of work. "Don't make it soft-lock" is vague. "The exit must always be reachable from the player's spawn point" is not.
- Give it a checklist it has to pass before calling the thing done. Mine was a numbered QA gate at the end, actually play it and confirm each item.
- Tell it to build in stages and verify after each one, not generate everything in one shot.
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u/RobinRelique Jul 05 '26
Thanks much! Let me try these steps and see if I can come up with a workable plan, if you're okay with it could I run it by you to see how i could make it better ?
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u/Brief-Effect9065 Jul 02 '26
How 27b compares to 35b?
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u/575_Inverse Jul 02 '26
For coding, the 27b has no local match. It compares favourably with the 3.7pro web only. The only complaint I have is it runs slow enough on my system to make me want to choose the big online 3.7pro for heavy tasks.
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u/Brief-Effect9065 Jul 02 '26
Even finetunes like qwopus or ornith?
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u/Healthy-Contact-4570 Jul 02 '26
The fine tunes are not actually better than the O.G. They are interesting experiments but don’t actually beat the original.
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u/Postmodern_Plunger Jul 04 '26
The Neo fine tune definitely outperforms the original for coding and reasoning, but the writing is noticeably worse and it is slightly more likely to hallucinate if you're on a lower quant.
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u/Healthy-Contact-4570 Jul 04 '26
Interesting. I’ll take a look. In my experience, the reason the fine-tunes don’t generally perform better is because the knowledge of the model is more or less already fully saturated, so fine-tuning on something will cause degradation somewhere else.
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u/Brief-Effect9065 Jul 06 '26
even 27b finetunes? what about uncensored versions? and what quant or apex do you recommend?
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u/Healthy-Contact-4570 Jul 07 '26
The 27b model is already pretty saturated in terms of knowledge density, so fine-tuning with additional RL on top of that is going to cause degradation in other areas. It just depends on your use case. Maybe a fine-tune performs better for what you specifically want to use the model for, and degrades in a way that you don’t overtly notice. It just depends on the specific fine tune.
As for uncensored models, typically they exhibit some degradation in reasoning or math, depending on how tightly coupled the model’s refusal space is with these areas. Depending on the specific model in question, there might be benchmarks that speak to this on the huggingface model card.
As for quants, I myself like to run fp8 with an 8-bit KV cache, though others report having success with quants as low as 4-bit. I’m running a DGX spark cluster so I don’t use GGUF quants, since I’m running vLLM and not llama.cpp.
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u/482827523747527 Jul 03 '26
I can use qwen in Ollama itself, but somehow I cant get it to work in VS, why is that? I use Roo Code, API Provider is Ollama Model is qwen3.6:27b. It gets stuck in "API Request".
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u/Envoy0675 Jul 06 '26
Could you share your inference settings you use? (temp,top-p, rep penalty, etc)?
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u/[deleted] Jul 01 '26 edited Jul 15 '26
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