Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

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

qwen.ai

Post reply on HN
Print view View original

81–90 of 482 posts

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

#81

Post by jadbox »

kroaton wrote: post #69
vibe42 wrote: post #65

Q4-Q5 quants of this model runs well on gaming laptops with 24GB VRAM and 64GB RAM. Can get one of those for around $3,500. Interesting pros/cons vs the new Macbook Pros depending on your prefs. And Linux runs better than ever on such machines.

A3B-35B is better suited for laptops with enough VRAM/RAM. This dense model however will be bandwidth limited on most cards. The 5090RTX mobile sits at 896GB/s, as opposed to the 1.8TB/s of the 5090 desktop and most mobile chips have way smaller bandwith than that, so speeds won't be incredible across the board like with Desktop computers.

I find A3B-35B as an ideal model for small local projects- definitely the best for me so far

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

#82

Post by Frannky »

jameson wrote: post #74

What competitive advantage does OpenAI/Anthropic has when companies like Qwen/Minimax/etc are open sourcing models that shows similar (yet below than OpenAI/Anthropic) benchmark results? Also, the token prices of these open source models are at a fraction of Anthropic's Opus 4.6[1] [1]: https://artificialanalysis.ai/models/#pricing

If these results are because of vampire attacks, the results will stop being so good when closed ones figure out how to pollute them when they are sucking answers.

Also, they are not exactly as good when you use them in your daily flow; maybe for shallow reasoning but not for coding and more difficult stuff. Or at least I haven't found an open one as good as closed ones; I would love to, if you have some cool settings, please share

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

#83

Post by tln »

sietsietnoac wrote: post #38

Generate an SVG of a pelican riding a bicycle: https://codepen.io/chdskndyq11546/pen/yyaWGJx Generate an SVG of a dragon eating a hotdog while driving a car: https://codepen.io/chdskndyq11546/pen/xbENmgK Far from perfect, but it really shows how powerful these models can get

The dragon image has issues like one eye, weird tail etc, but the pelican is imo perfect -- the best I've seen!

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

#84

Post by fnordpiglet »

jameson wrote: post #74

What competitive advantage does OpenAI/Anthropic has when companies like Qwen/Minimax/etc are open sourcing models that shows similar (yet below than OpenAI/Anthropic) benchmark results? Also, the token prices of these open source models are at a fraction of Anthropic's Opus 4.6[1] [1]: https://artificialanalysis.ai/models/#pricing

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 R&D in the present is beneficial to them in forcing them to make a better and better product especially in value add space.

Finally, particularly for Anthropic, they are going for the more trustworthy shop. Even ali is hosting pay frontier models for service revenue, but if you’re not a Chinese shop, would you really host your production code development workload on a Chinese hosted provider? OpenAI is sketchy enough but even there I have a marginal confidence they aren’t just wholesale mining data for trade secrets - even if they are using it for model training. Anthropic I slightly trust more. Hence the premium. No one really believes at face value a Chinese hosted firm isn’t mass trolling every competitive advantage possible and handing back to the government and other cross competitive firms - even if they aren’t the historical precedent is so well established and known that everyone prices it in.

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

#85

Post by kkzz99 »

Aurornis wrote: post #35
GaggiX wrote: post #20

Earlier quoted context omitted.

At 4-bit quantization it should already fit quite nicely.

Unfortunately not with a reasonable context length.

It really depends on what you think a reasonable context length is, but I can get 50k-60k on a 4090.

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

#86

Post by bityard »

UncleOxidant wrote: post #50
anonzzzies wrote: post #11

I wish that all announcements of models would show what (consumer) hardware you can run this on today, costs and tok/s.

For Qwen3.5-27b I'm getting in the 20 to 25 tok/sec range on a 128GB Strix Halo box (Framework Desktop). That's with the 8-bit quant. It's definitely usable, but sometimes you're waiting a bit, though I'm not finding it problematic for the most part. I can run the Qwen3-coder-next (80b MoE) at 36tok/sec - hoping they release a Qwen3.6-coder soon.

I have a Framework Desktop too and 20-25 t/s is a lot better than I was expecting for such a large dense model. I'll have to try it out tonight. Are you using llama.cpp?

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

#87

Post by yrds96 »

sietsietnoac wrote: post #38

Generate an SVG of a pelican riding a bicycle: https://codepen.io/chdskndyq11546/pen/yyaWGJx Generate an SVG of a dragon eating a hotdog while driving a car: https://codepen.io/chdskndyq11546/pen/xbENmgK Far from perfect, but it really shows how powerful these models can get

I wonder if this became a so well known "benchmark" that models already got trained for it.

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

#88

Post by zozbot234 »

ndriscoll wrote: post #70
Aurornis wrote: post #23

Earlier quoted context omitted.

The 27B model they release directly would require significant hardware to run natively at 16-bit: A Mac or Strix Halo 128GB system, multiple high memory consumer GPUs, or an RTX 6000 workstation card. This is why they don’t advertise which consumer hardware it can run on: Their direct release that delivers these results cannot fit on your average consumer system. Most consumers don’t run the model they release direct…

Note that you could also run them on AMD (and presumably Intel) dGPUs. e.g. I have a 32GB R9700, which is much cheaper than a 5090, and runs 27B dense models at ~20 t/s (or MoE models with 3-4B active at ~80t/s). I expect an Arc B70 would also work soon if it doesn't already, and would likely be the price/perf sweet spot right now. My R9700 does seem to have an annoying firmware or driver bug[0] that causes the fan t…

Yup, I suppose that these smaller, dense models are in the lead wrt. fast inference with consumer dGPUs (or iGPUs depending on total RAM) with just enough VRAM to contain the full model and context. That won't give you anywhere near SOTA results compared to larger MoE models with a similar amount of active parameters, but it will be quite fast.

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

#89

Post by bityard »

mottosso wrote: post #18
underlines wrote: post #16

Earlier quoted context omitted.

depends on format, compute type, quantization and kv cache size.

Specs for whatever they used to achieve the benchmarks would be a good start.

The benchmarks in the model card are purported to be measurements of model quality (ability to perform tasks with few errors), not speed.

They almost certainly run these benchmarks on their own cloud infrastructure (Alibaba afaik), which is typically not hardware that even the most enthusiastic homelab hobbyist can afford.

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

#90

Post by AJ007 »

fnordpiglet wrote: post #84
jameson wrote: post #74

What competitive advantage does OpenAI/Anthropic has when companies like Qwen/Minimax/etc are open sourcing models that shows similar (yet below than OpenAI/Anthropic) benchmark results? Also, the token prices of these open source models are at a fraction of Anthropic's Opus 4.6[1] [1]: https://artificialanalysis.ai/models/#pricing

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…

Not sure how your last point matters if 27b can run on consumer hardware, besides being hosted by any company which the user could certainly trust more than anthropic.

OpenAI & Anthropic are just lying to everyone right now because if they can't raise enough money they are dead. Intelligence is a commodity, the semiconductor supply chain is not.

Post reply on HN