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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

#171

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

I just ran Unsloth's Q4_K_M quant on Macbook Pro M1 Max (2021), 32 GB RAM. Getting about 9 tok/s. Reported RAM usage 15.9 GB. It's okay, but a bit slow for my taste.

An example of a query and a response is below. It was done with 9.09 tok/s, 893 tokens, 7.56 seconds to first token, thought for 1 minute and 9 seconds.

Question: I gave the model this truth table and asked to tell me what boolean function would produce the output given A, B as inputs.

  | A | B | Output |
  | :--- | --- | ---: |
  | (T) | (F)  | (T) |
  | (T) | (T) | (F) |
  | (F) | (F) | (F) |
  | (F) | (T) | (F) |
Answer: (note this is part of a larger conversation, where I was getting it to help me with some boolean logic):

  The boolean condition for your table is simply:

  ```rust
  A && !B
  ```

  ### Why?
  Your table shows the output is `true` **only** when `A` is true and `B` is  false. That's exactly what `A && !B` expresses.

  | A | B | `A && !B` | Output |
  |---|---|-----------|--------|
  |  (T) |  (F) | `T && T = T` |   |
  |  (T) |  (T) | `T && F = F` |   |
  |  (F) |  (F) | `F && T = F` |   |
  |  (F) |  (T) | `F && F = F` |   |

  ### In Rust:
  ```rust
  if A && !B {
      // output is true
  } else {
      // output is false
  }
  ```

  This is the most direct and idiomatic way to express that logic. Let me know if you need it adapted back to your `live_ticks` / `on_disconnect` context!

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

#172
post #27

Earlier quoted context omitted.

I get ~5 tokens/s on an M4 with 32G of RAM, using: llama-server \ -hf unsloth/Qwen3.6-27B-GGUF:Q4_K_M \ --no-mmproj \ --fit on \ -np 1 \ -c 65536 \ --cache-ram 4096 -ctxcp 2 \ --jinja \ --temp 0.6 \ --top-p 0.95 \ --top-k 20 \ --min-p 0.0 \ --presence-penalty 0.0 \ --repeat-penalty 1.0 \ --reasoning on \ --chat-template-kwargs '{"preserve_thinking": true}' 35B-A3B model is at ~25 t/s. For comparison, on an A100 (~RTX…

We also made some dynamic MLX ones if they help - it might be faster for Macs, but llama-server definitely is improving at a fast pace. https://huggingface.co/unsloth/Qwen3.6-27B-UD-MLX-4bit

What exactly does the .sh file install? How does it compare to running the same model in, say, omlx?

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

#173
post #90

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…

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.

The challenge is token speed. I did some local coding yesterday with qwen3.6 35b and getting 10-40 tokens per second means that the wall time is much longer. 20 tokens per second is a bit over a thousand tokens per minute, which is slower than the the experience you get with Claude Code or the opus models.

Slower and worse is still useful, but not as good in two important dimensions.

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

#174
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…

at what point do model providers optimize for the "pelican riding a bicycle" test so they place well on Simon's influential benchmark? :-)

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

#175

I 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…

Because when you pay for a subscription they don't silently quantize the model a few week after release, and you can no longer get the full model running.

Otherwise no need for full fp16, int8 works 99% as well for half the mem, and the lower you go the more you start to pay for the quants. But int8 is super safe imo.

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

#177

Earlier quoted context omitted.

Why would you though? And by the way: Thanks for relentlessly holding new models’ feet to the pelican SVG fire.

Because I want to read about Qwen, not someone's one-off vibe test followed by 1:1 conversations. (case in miniature here: which is the last comment in this thread that says something about Qwen? The root post. Is that fun policing? Yes, apologies.)

I think it's to help drive traffic to his blog now that he's accepted sponsors in the header of every page. I do see this pelican thing come up from him on every model post that gets released.

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

#178
post #133

Earlier quoted context omitted.

Qwen3.5-27B with a 4bit quant can be run on a 24G card with no problem. With 2 Nvidia L4 cards and some additional vllm flags, i am serving 10 developers at 20-25tok/sek, off-peak is around 40tok/sek. Developers are ok with that performance, but ofc they requested more GPU's for added throughput.

question: why not use something like Claude? is it for security reasons?

Some people would rather not hand over all of their ability to think to a single SaaS company that arbitrarily bans people, changes token limits, tweaks harnesses and prompts in ways that cause it to consume too many tokens, or too few to complete the task, etc.

I don't use any non-FLOSS dev tools; why would I suddenly pay for a subscription to a single SaaS provider with a proprietary client that acts in opaque and user hostile ways?

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

#179
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…

at what point do model providers optimize for the "pelican riding a bicycle" test so they place well on Simon's influential benchmark? :-)

They almost certainly are, even if unknowingly, because HN and all blogs get piped continuously into all models' training corpus.

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

#180

Earlier 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 :)

what are you using for web search?
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