TIL that our corporate network site blocker classifies qwen.ai as a sex site…
Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model
241–250 of 482 posts
Re: Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model
#242TIL that our corporate network site blocker classifies qwen.ai as a sex site…
.. that is what they tell YOU
Re: Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model
#243I wish that all announcements of models would show what (consumer) hardware you can run this on today, costs and tok/s.
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…
Given the current best open-weight model (Kimi 2.6) is 1T A32B, I wonder how long we’ll have to wait for hardware like strix halo or gdx spark to be able to run it.
Re: Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model
#244The 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…
These are the stupidest things to cleave to.
Re: Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model
#245Been using Qwen 3.6 35B and Gemma 4 26B on my M4 MBP, and while it’s no Opus, it does 95% of what I need which is already crazy since everything runs fully local.
Do you use it with ollama? Or something else?
Re: Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model
#246What 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…
> but if you’re not a Chinese shop, would you really host your production code development workload on a Chinese hosted provider?
As opposed to an US-american shop? Yup, sure, why not? It's the same ballpark.
Re: Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model
#247Earlier quoted context omitted.
i have a Strix Halo machine typically those dense models are too slow on Strix Halo to be practical, expect 5-7 tps you can get an idea by looking at other dense benchmarks here: https://strixhalo.zurkowski.net/experiments - i'd expect this model to be tested here soon, i don't think i will personally bother
This one is around 250 t/s prefill and 12.4 generation in my testing.
interesting, might be worth having around although it is still pretty slow
Re: Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model
#248Are there benchmarks of this / what’s the best way to compare it against paid models? With all the rate limiting in Claude/Copilot/etc, running locally is more and more appealing.
Re: Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model
#249What 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…
> For coding often quality at the margin is crucial even at a premium
For some problems, sure, and when you are stuck, throwing tokens at Opus is worthwhile.
On the other hand, a $10/month minimax 2.7 coding subscription that literally never runs out of tokens will happily perform most day-to-day coding tasks
Re: Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model
#250Earlier quoted context omitted.
Yea, this is currently the confusing part of running local models for newbies: Even after you have decided which model you want to run, and which org's quantizations to use (let's just assume Unsloth's for example), there are often dozens of quantizations offered, and choosing among them is confusing. Say you have a GPU with 20GB of VRAM. You're probably going to be able to run all the 3-bit quantizations with no pro…
We made Unsloth Studio which should help :) 1. Auto best official parameters set for all models 2. Auto determines the largest quant that can fit on your PC / Mac etc 3. Auto determines max context length 4. Auto heals tool calls, provides python & bash + web search :)
Thanks for that. Did you notice that the unsloth/unsloth docker image is 12GB? Does it embed CUDA libraries or some default models that justifies the heavy footprint?