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Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model

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Re: Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model

#351

Post by syndacks »

simonw wrote: post #106

The pelican is excellent for a 16.8GB quantized local model: https://simonwillison.net/2026/Apr/22/qwen36-27b/ I ran it on an M5 Pro with 128GB of RAM, but it only needs ~20GB of that. I expect it will run OK on a 32GB machine. Performance numbers: Reading: 20 tokens, 0.4s, 54.32 tokens/s Generation: 4,444 tokens, 2min 53s, 25.57 tokens/s I like it better than the pelican I got from Opus 4.7 the other day: https://si…

it seemed HN was moving the right direction when we added the "no AI comments", and yet, every single post about a new model is from you and your pelican. it's tired. please stop, it adds no value and has become cliche.

Re: Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model

#352

Post by fshen »

datadrivenangel wrote: post #279

So far I'm unimpressed for local inference. got 11 tokens per second on omlx on an M5 Pro with 128gb of ram, so it took an hour to write a few hundred lines of code that didn't work. Opus and Sonnet in CC the same task successfully in a matter of minutes. The 3.6:35b model seemed okay on ollama yesterday. Need to check out other harnesses for this besides claude code, but the local models are just painfully slow.

I use the same computer as you do. m5 can run faster:

pip install mlx_lm

python -m mlx_vlm.convert --hf-path Qwen/Qwen3.6-27B --mlx-path ~/.mlx/models/Qwen3.6-27B-mxfp4 --quantize --q-mode mxfp4 --trust-remote-code

mlx_lm.generate --model ~/.mlx/models/Qwen3.6-27B-mxfp4 -p 'how cpu works' --max-tokens 300

Prompt: 13 tokens, 51.448 tokens-per-sec Generation: 300 tokens, 35.469 tokens-per-sec Peak memory: 14.531 GB

Re: Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model

#353

Post by 0xbadcafebee »

syndacks wrote: post #351
simonw wrote: post #106

The pelican is excellent for a 16.8GB quantized local model: https://simonwillison.net/2026/Apr/22/qwen36-27b/ I ran it on an M5 Pro with 128GB of RAM, but it only needs ~20GB of that. I expect it will run OK on a 32GB machine. Performance numbers: Reading: 20 tokens, 0.4s, 54.32 tokens/s Generation: 4,444 tokens, 2min 53s, 25.57 tokens/s I like it better than the pelican I got from Opus 4.7 the other day: https://si…

it seemed HN was moving the right direction when we added the "no AI comments", and yet, every single post about a new model is from you and your pelican. it's tired. please stop, it adds no value and has become cliche.

We like the pelican posts.

Re: Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model

#354

Post by spmurrayzzz »

objektif wrote: post #224

Does anyone know good provider for low latency llm api provider? We tried to look at Cerebras and Groq but they have 0 capacity right now. GPT models are too slow for us at the moment. Gemini are better but not really at same level as GPT.

This depends a bit on your cost sensitivity and what model families you want support for, but Baseten and Fireworks have been my goto.

Currently Baseten has ~610ms TTFT and ~82 tk/s for Kimi K2.6, which is roughly 2x the throughput of GPT-5.4 (per their openrouter stats). GLM 5 is slightly slower on both metrics, but still strong.

Re: Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model

#355

Post by Zetaphor »

fnordpiglet wrote: post #262
ozgrakkurt wrote: post #234

Earlier quoted context omitted.

I just assume any of those companies would steal my work and wouldn't care about it. Everything they have done so far indicates this. Running your own is the only option unless you really trust them or unless you have the option to sue them like some big companies can. Or if you don't really care then you can use the chineese one since it is cheaper. What makes you trust Anthropic more than Alibaba?

There’s a difference between stealing for model training and direct monitoring of actionable trade secrets and corporate espionage. Anthropic and OpenAI wouldn’t do this simply because they would be litigated out of existence and criminally investigated if they did. In China it’s an expected part of the corporate and legal structure with virtually no recourse for a foreign firm and when it’s in states interest domest…

How exactly do you propose that a local weights model that I can run without an internet connection is going to exfiltrate my trade secrets to the Chinese government?

Re: Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model

#356

Post by pixelatedindex »

syndacks wrote: post #351
simonw wrote: post #106

The pelican is excellent for a 16.8GB quantized local model: https://simonwillison.net/2026/Apr/22/qwen36-27b/ I ran it on an M5 Pro with 128GB of RAM, but it only needs ~20GB of that. I expect it will run OK on a 32GB machine. Performance numbers: Reading: 20 tokens, 0.4s, 54.32 tokens/s Generation: 4,444 tokens, 2min 53s, 25.57 tokens/s I like it better than the pelican I got from Opus 4.7 the other day: https://si…

it seemed HN was moving the right direction when we added the "no AI comments", and yet, every single post about a new model is from you and your pelican. it's tired. please stop, it adds no value and has become cliche.

Wholly disagree. This a comment made by a person about an AI topic. Not an AI bot commenting on an article, which (as I understand it) is what “no AI comments” is saying.

Plus it’s a test that gives varied enough performance across multiple LLMs that it is a good barometer for how well it can think through the steps. Never mind the fact that most people can’t draw a bike from memory. The whole thing is hilarious!

Re: Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model

#357

Post by Zetaphor »

fnordpiglet wrote: post #149
rohansood15 wrote: post #91

Earlier quoted context omitted.

Most code is not P99 though. Also, have you considered that your trust in Anthropic and distrust in China may not be shared by many outside the US? There's a reason why Huawei is the largest supplier of 5G hardware globally.

I find it hard to believe anyone who has ever done business inside China doesn’t know that the structure of Chinese business is built around massive IP theft and repurposing on a state wide systematic level. It’s not a nationalism point, it’s an objective and easily verified truth. Most code is not P99, but companies pay a premium to produce code that is. That’s my point.

I'll ask you the same thing I asked the other guy. How is a an open weights model that I can run on my own hardware without an internet connection going to exfiltrate my trade secrets to the Chinese government?

Re: Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model

#358

Post by Zetaphor »

DiogenesKynikos wrote: post #346
fnordpiglet wrote: post #84

Earlier quoted context omitted.

For coding often quality at the margin is crucial even at a premium. It’s not the same as cranking out spam emails or HN posts at scale. This is why the marginal difference between your median engineer and your P99 engineer is comp is substantial, while the marginal comp difference between your median pick and packer vs your P99 pick and packer isn’t. I’d also say it keeps the frontier shops competitive while costing…

Are you claiming that major Chinese cloud providers like Tencent and Alibaba are pilfering trade secrets from their customers' data? To my knowledge, there's no evidence for that whatsoever. If it were true and came out, it would instantly tank their cloud businesses (which is why they don't do it, and why AWS, Azure, etc. also don't do it). If it were to happen, Chinese law does offer recourse, including to foreign…

In every one of these threads for a new Chinese open weights model, it's always the same tired discussion of how this is all actually a psyop by the Chinese government to undermine US interests and it can't answer questions about Tienanmen Square.

Meanwhile I'm over here solving real world business problems with a model that I can securely run on-prem and not pay out the nose for cloud GPU inference. And then after work I use that same model to power my personal experiments and hobby projects.

There are no Chinese labs with different financial and political motivations, there's only "China" the monolith. The last thread for Qwen's new hosted model was full of folks talking about how "China" is no longer releasing open weights models, when the next day Moonshot AI releases Kimi 2.6. A few days later and here's Qwen again with another open release.

For some reason this country gets what I assume are otherwise smart Americans to just completely shut off their brains and start repeating rhetoric.

Re: Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model

#359

Post by thot_experiment »

zargon wrote: post #344
harkh wrote: post #268

Earlier quoted context omitted.

I agree, what would you say are currently the best local options?

For FIM, there's Qwen3 Coder Next. Although Mistral's model card seems to indicate that Devstral 2 doesn't support FIM, it seems very odd that it wouldn't. I have been meaning to test it.

Qwen Coder 30B A3B is far better than Qwen Coder Next imo. I may have inference issues or it's just a problem with running Coder Next at IQ4 XS, vs Q8 for the earlier/smaller model but I don't find the 80B to be much better at coding, even in instruct mode, and the insane speed and low latency of the smaller model is way more useful. Good one-line completions often happen in 300ms.

Re: Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model

#360

Post by ycombinatornews »

syntaxing wrote: post #225
Someone1234 wrote: post #220

Earlier quoted context omitted.

You've got me curious. Two questions if I may: - What kind of tasks/work? - How is either Qwen/Gemma wired up (e.g. which harness/how are they accessed)? Or to phase another way; what does your workflow/software stack look like?

1. Qwen is mostly coding related through Opencode. I have been thinking about using pi agent and see if that works better for general use case. The usefulness of *claw has been limited for me. Gemma is through the chat interface with lmstudio. I use it for pretty much everything general purpose. Help me correct my grammar, read documents (lmstudio has a built in RAG tool), and vision capabilities (mentioned below, jo…

Curious how do you run opencode and qwen locally? Few times I tried it responds back with some nonsense. Chat, say, through ollama works well.
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