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 directly. They run a quantized model that uses a lower number of bits per weight.
The quantizations come with tradeoffs. You will not get the exact results they advertise using a quantized version, but you can fit it on smaller hardware.
The previous 27B Qwen3.5 model had reasonable performance down to Q5 or Q4 depending on your threshold for quality loss. This was usable on a unified memory system (Mac, Strix Halo) with 32GB of extra RAM, so generally a 64GB Mac. They could also be run on an nVidia 5090 with 32GB RAM or a pair of 16GB or 24GB GPUs, which would not run as fast due to the split.
Watch out for some of the claims about running these models on iPhones or smaller systems. You can use a lot of tricks and heavy quantization to run it on very small systems but the quality of output will not be usable. There is a trend of posting “I ran this model and this small hardware” repos for social media bragging rights but the output isn’t actually good.
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 problem, but which one do you choose? Unsloth offers[1] four of them: UD-IQ3_XXS, Q3_K_S, Q3_K_M, UD-Q3_K_XL. Will they differ significantly? What are each of them good at? The 4-bit quantizations will be a "tight squeeze" on your 20GB GPU. Again, Unsloth steps up to the plate with seven(!!) choices: IQ4_XS, Q4_K_S, IQ4_NL, Q4_0, Q4_1, Q4_K_M, UD-Q4_K_XL. Holy shit where do I even begin? You can try each of them to see what fits on your GPU, but that's a lot of downloading, and then...
Once you [guess and] commit to one of the quantizations and do a gigantic download, you're not done fiddling. You need to decide at the very least how big a context window you need, and this is going to be trial and error. Choose a value, try to load the model, if it fails, you chose too large. Rinse and repeat.
Then finally, you're still not done. Don't forget the parameters: temperature, top_p, top_k, and so on. It's bewildering!
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 :)
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
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}'
I haven't tested the 27B model yet, but 35B-A3B often gets off rails after 15k-20k tokens of context. You can have it to do basic things reliably, but certainly not at the level of "frontier" models.
The 4-bit quants are far from lossless. The effects show up more on longer context problems.
> You can probably even go FP8 with 5090 (though there will be tradeoffs)
You cannot run these models at 8-bit on a 32GB card because you need space for context. Typically it would be Q5 on a 32GB card to fit context lengths needed for anything other than short answers.
You probably can actually. Not saying that it would be ideal but it can fit entirely in VRAM (if you make sure to quantize the attention layers). KV cache quantization and not loading the vision tower would help quite a bit. Not ideal for long context, but it should be very much possible.
I addressed the lossless claim in another reply but I guess it really depends on what the model is used for. For my usecases, it's nearly lossless I'd say.
This isn't the first open-weight LLM to be released. People tend to get a feel for this stuff over time.
Let me give you some more baseless speculation: Based on the quality of the 3.5 27B and the 3.6 35B models, this model is going to absolutely crush it.
TLDR: If you have 14GB of VRAM, you can try out this model with a 4-bit quant.
Tokens per second is an unreasonable ask since every card is different, are you using GGUF or not, CUDA or ROCm or Vulkan or MLX, what optimizations are in your version of your inference software, flags are you running, etc.
Note that it's a dense model (the Qwen models have another value at the end of the MoE model names, e.g. A3B) so it will not run very well in RAM, whereas with a MoE model, you can spill over into RAM if you don't have enough VRAM, and still have reasonable performance.
Using these models requires some technical know-how, and there's no getting around that.
This will only run on server hardware, some workstation GPUs, or some 128GB unified memory systems.
It’s a situation where if you have to ask, you can’t run the exact model they released. You have to wait for quantizations to smaller sizes, which come in a lot of varieties and have quality tradeoffs.
It's also a section that, with hope, becomes obsolete sometime semi soon-ish.
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
Interesting pros/cons vs the new Macbook Pros depending on your prefs.
And Linux runs better than ever on such machines.
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.
Then again, I was looking in the UK, maybe prices are extra inflated there.
Friendly reminder: wait a couple weeks to judge the ”final” quality of these free models. Many of them suffer from hidden bugs when connected to an inference backend or bad configs that slow them down. The dev community usually takes a week or two to find the most glaring issues. Some of them may require patches to tools like llama.cpp, and some require users to avoid specific default options.
Gemma 4 had some issues that were ironed out within a week or two. This model is likely no different. Take initial impressions with a grain of salt.
The bugs come from the downstream implementations and quantizations (which inherit bugs in the tools).
Expect to update your tools and redownload the quants multiple times over 2-4 weeks. There is a mad rush to be first to release quants and first to submit PRs to the popular tools, but the output is often not tested much before uploading.
If you experiment with these on launch week, you are the tester. :)
I’m excited to try out the MLX version to see if 32GB of memory from a Pro M-series Mac can get some acceptable tok/s with longer context. HuggingFace has uploaded some MLX versions already.
It's been a while since I tried it, but I think I was getting around 12-15 tokens per second an that feels slow when you're used to the big commercial models. Whenever I actually want to do stuff with the open source models, I always find myself falling back to OpenRouter.
I tried Intel/Qwen3.6-35B-A3B-int4-AutoRound on a DGX Spark a couple days ago and that felt usable speed wise. I don't know about quality, but that's like running a 3B parameter model. 27B is a lot slower.
I'm not sure if I "get" the local AI stuff everyone is selling. I love the idea of it, but what's the point of 128GB of shared memory on a DGX Spark if I can only run a 20-30GB model before the slow speed makes it unusable?
It’s not a surprise that models are leapfrogging each other when the engineers are able to incorporate better code examples and reasoning traces, which in turn bring higher quality outputs.
Every release is accompanied by claims of being as good as Sonnet or Opus, but when I try them (even hosted full weights) they’re far from it.
Impressive for the size, though!
if you can't afford to do that, look at a lot of them, eg. on artificialanalysis.com they merge multiple benchmarks across weighted categories and build an Intelligence Score, Coding Score and Agentic score.
GLM 5 scores 5% on the semi-private set, compared to SOTA models which hover around 80%.
But when actually employed to write code they will fall over when they leave that specific domain.
Basically they might have skill but lack wisdom. Certainly at this size they will lack anywhere close to the same contextual knowledge.
Still these things could be useful in the context of more specialized tooling, or in a harness that heavily prompts in the right direction, or as a subagent for a "wiser" larger model that directs all the planning and reviews results.
What matters is the motion in the tokens
Gemini flash was just as good as pro for most tasks with good prompts, tools, and context. Gemma 4 was nearly as good as flash and Qwen 3.6 appears to be even better.
$ llama-server --version
version: 8851 (e365e658f)
$ llama-batched-bench -hf unsloth/Qwen3.6-27B-GGUF:IQ4_XS -npp 1000,2000,4000,8000,16000,32000 -ntg 128 -npl 1 -c 34000
| PP | TG | B | N_KV | T_PP s | S_PP t/s | T_TG s | S_TG t/s | T s | S t/s |
|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
| 1000 | 128 | 1 | 1128 | 1.529 | 654.11 | 3.470 | 36.89 | 4.999 | 225.67 |
| 2000 | 128 | 1 | 2128 | 3.064 | 652.75 | 3.498 | 36.59 | 6.562 | 324.30 |
| 4000 | 128 | 1 | 4128 | 6.180 | 647.29 | 3.535 | 36.21 | 9.715 | 424.92 |
| 8000 | 128 | 1 | 8128 | 12.477 | 641.16 | 3.582 | 35.73 | 16.059 | 506.12 |
| 16000 | 128 | 1 | 16128 | 25.849 | 618.98 | 3.667 | 34.91 | 29.516 | 546.42 |
| 32000 | 128 | 1 | 32128 | 57.201 | 559.43 | 3.825 | 33.47 | 61.026 | 526.47 |For anyone invested in running LLMs at home or on a much more modest budget rig for corporate purposes, Gemma 4 and Qwen 3.6 are some of the most promising models available.