It would be nice if unsloth published GGUFs would use a version number or something, because now I have multiple different files on local storage that otherwise have exactly the same name.
"Qwen3.8-27B-UD-Q8_K_XL.gguf" for instance.
The one downloaded at least 4 days ago is a different thing and is NOT the "Dynamic 3.0" GGUF which I am now downloading, which I presume will have a different sha256 checksum?
The unsloth page says dynamic 3.0 is released "today", but I have an older copy of qwen3.8 27B Q8 which I downloaded, if I remember right, at least 4-5 days ago...
there's metadata in a GGUF (as can be seen when loading one into llama-server with verbose level 4) but it doesn't appear to show what the expected sha256 of the file is.
Well that would not work as the metadata is also part of the sha, and if you have a sha predictably contain or hash to itself you have a bad hashing algorithm. (Unless of course you mean sha of the weigths and not the gguf itself)
I have just run a sha256 checksum on both copies now, the one I downloaded 4-5 days ago, and the one that's on the unsloth huggingface page released today, and will see if they're the same or different.
(downloaded in the last few hours after the announcement of dynamic 3.0)
I think they said they were keeping the old UD 2.0 quant for the larger sizes? So maybe they kept those the same and simply reuploaded them. They said the newer UD 3.0 quant performed worse on some things for the higher quants. So now it's a mix of UD 3.0 and UD 2.0.
I mostly use local models when the data has personal information. Earlier this year, I felt the coding quality was still not as good as Claude Code.
One thing that works for me is to ask the local model to make some fake data with the same format, let Claude Code work on the fake data, and then bring the code back and run it locally on the real data.
This way the real data never leaves my machine, but I can still use a stronger model for most of the coding.
Hey Unsloth, your gguf are the first ones I look for when I want to download a gguf model.
Today I was trying in fact to see, what's the smallest Qwen3.8-27B that I could run and get good results, say restricting it to 16GB of ram.. so I went, pick up the Qwen3.8-27B-UD-IQ2_XXS.gguf and them BAM, error on MTP... now I understand why after reading your announcement.
Beyond the space saving, why removing the MTP? improves speed exactly for the group that could benefit from it.
The reason for running those insanely low quants is to fit in extremely limited memory budgets. The first thing you sacrifice is speed, then context and accuracy (up to you in which order). IQ2_XXS and below is desperate/proof of concept territory. If you have a spare half gig for the MTP drafter, run a larger quant instead, it will be less incoherent, and damn the speed, it won't be garbage at least. Only around Q4 I'd allocate the comparative luxury of more memory for a speed increase. At least on a dense model. MTP makes a lot more sense (but helps statistically a bit less) on an MoE.
Hey we did not remove the MTP for sizes above 8GiB - but yes for small GGUFs under 8 ish GiB, we removed the MTP module (IQ2_XXS and lower), because it's 500MiB to 750MiB in size, and on small 8 GiB machines, even 500MiB is needed.
As someone in the comments said we made a separate Q4_0 MTP if that's helpful so you can use that.
But I would suggest using UD-IQ3_XXS for 10.9GB for 16GB machines or Q2_K_XL
Daniel, question I got the Qwen3.8-27B-UD-Q2_K_XL.gguf from https://huggingface.co/unsloth/Qwen3.8-27B-GGUF?show_file_in... and continue with my testing, but the model quickly felt into a loop of asking the same thing over and over again, I have seen the MOE do that but not the dense ones.
And I had similar experiences when Qwen3.8-27B unsloth images just came out with the full Q8_K_XL, I'm using an AMD setup which has modifications to save to disk the kv, but your (assuming you are part of the unsloth team) for some reason have been giving me similar issues.
It can be something in my setup, there is a very high chance of that, but the previous 3.6 images from qwen, the 27B, the 31A3 and 122 they are all unsloth and did work on my setup without issues...
Again could be my setup... let me know if there is any data I can supply to you to debug if needed.
> We also removed the MTP module from smaller quants under UD-Q2_K_XL (8.37GB and lower) to converse around 500MB of disk space - you can use the Q4_0 MTP separate module if needed
Q2 quantization is basically giving a capable model a lobotomy. It will not accurately represent how smart or capable something like qwen 3.8 27B in Q8 will be.
Are there benchmarks for the various Qwen3.8-27B quants that actually measure writing code, maybe even with multiple steps? Low KL divergence does not mean much when the model gets stuck in doom loops all the time.
I could of course download and test myself, but that would take days with my internet connection.
We made something called Divergence-300 @32 (and later @512) which tests actual inference across 32 tokens on a held out test (Terminal Bench, DeepSWE, Math etc)
Interestingly, it also seems to tend toward self-correcting, which makes lower quantizations borderline usable. There'll be more faffing around, but still converging toward a solution. I wonder if that's a deliberate product of its RL.
Purely an anecdote, but I've found Qwen3.8-27b doesn't doom loop like previous Qwen models would. With that said, it absolutely thinks in circles- it'll prepare to do something, say it is now ready to do it, then follow that with three paragraphs that all start with Acutally... Oh wait, I should check first... Hmm, hmm... I should stop guessing and just do it. Okay, I'm ready to do the thing now... Actually, wait...
It takes forever, but it does actually get around to making things work, and it is more thorough and produces better code than previous qwen models. You just need to let it run quite awhile.
Since it seems like this not only improved sizes but also performance I can't wait for some benchmarks and comparisons. If you don't have a separate GPU for inference, every single GB matters so a comparison between specific Q4 Quants is really interesting to me.
Currently I very much can't decide between going for a bit of a lower Q4 Quant to squeeze out a bit of buffer and ctx or wondering if a slightly higher (IQ4_XS vs Q4_K_M/XL) is worth it
I tried some 1-bit, 2-bit, and bonsai quants against closed eval sets. They were essentially useless for my case. The little errors accumulate and send the whole output off track quickly.
If you had some use case with very small output sequences they could be interesting to try. I think dropping down to a 9B-class model would produce better results for most cases.
Not 1-bit, but I’m getting pretty good results with some light coding using unsloth’s previous 2-bit quant of qwen3.8-27b. With these new quants i may be able to bump up to 3bit, tho it’s already running so slow (15tok/s average for the first 32k of context) that the speed hit might make it not worth the extra smarts
Might be off-topic but: is it possible to perform such a quantization on Apple devices? Something like Mac Studio Ultra M1 (even if it would take weeks/months)?
Unsloth use a property dataset they don't release, however you can indeed create quantisation locally on your machine and it's pretty easy, llama.cpp comes with everything you need.
Just quantizing takes seconds-to-minutes, llama.cpp provides a nice tool[0]. Improving quality is then a matter of picking specific tensors to maintain at higher accuracy, checking on representative data, and repeating.
What size context are you able to squeeze in with less than 2gb of headroom? I have had some luck using a quantized kv cache but i fear that also decreases overall quality.
Yes, and if you have the PCIe lanes (say, an x16 lane - actually delivering 16 lanes! - to each GPU) it's also quite performant - it's called a tensor split in llama-server.
If your motherboard/cpu doesn't actually have those (few do outside some xeons, epycs and threadrippers) you can still do it - it's called a layer split and will work even with 1 lane per GPU. Each GPU will work at its maximum speed, but only 1 will be active at any given instant - imagine a relay race.
(Didn't mention which PCIe generation - obviously the higher the better. At v4 and up, even 8 lanes per GPU would be enough for a performant tensor 4-way split)
Edit:
If you have more than 1 user at a time, the GPU can actually all be working all the time, if there are enough parallel requests to serve. But you need enough KV cache for all the sessions you're running in parallel.
Yes, you can. Ideally though, you want to minimize the number of cards and maximize the amount of memory in each card.
Using multiple cards is one of the things that the models and software that Unsloth releases does really well in terms of ease of use and relatively good performance.
I've run 4, 6, and 8. Adding more video cards increases total available memory, so you can load larger models, but there are definitely some drawbacks.
More cards = more communication over PCIe. The prompts don't come in and just get magically split between each card, they move sequentially through them.
Also, with 2x24GB cards you don't really have 48GB of usable memory to load a model, closer to ~42GB + context.
And then there are power concerns, motherboard limitations (PCIe slots and lanes - a lot of motherboards with multiple 16x PCIe slots don't actually have 16x lanes to each of those slots), and more. 8x GPUs are going to easily draw 2000W on their own, if not substantially more. You'll need wiring and a circuit that can support 3000W without a risk of starting a fire in your wall.
For $5k, a single 32GB 5090 might be a better choice for a lot of people versus 4x3090s with 24GB each. It will definitely perform substantially better on smaller 27B models.
For hardware:
A good motherboard with lots of PCIe lanes (7x full 16x PCIe 4.0), DDR4 support, etc:
Add in a 3xxx series Threadripper PRO, 128 or 256GB of DDR4 (going higher becomes really expensive), and a ~1400 watt power supply. You can underpower/undervolt Nvidia cards really easily, and capping them at 250W loses you minimal performance.
Of course. Models don't actually require VRAM. Nor do they require regular RAM. You could have 1 GB of RAM and swap the model to disk as you need different parts of it. And if you didn't have enough disks you could access weights via a network connection.
You don’t even need electricity. You could print the model weights onto millions of sheets of paper, and hire a team of carrier pigeons to fly them into your office one by one. No VRAM!
It seems the NVFP4 quants have a preview version of this Unsloth Dynamic 3.0. Is this close to the finished version, or would it be better to switch to one of the newer quants?
One agent typically blocks the others on a local device because the GPU is already completely utilized either in terms of memory or compute. You can have true parallelism at home, but you need an absurd amount of resources. It's not a simple threading problem.
I have no problem running two or three sequences of qwen 27B with a 3090. It's basically the recommended way, LLM inference without batching is super inefficient.
The typical bottleneck to wider batching on consumer hardware is memory capacity for the KV-cache, not compute (even unified memory/iGPU-based platforms have enough compute to allow for some batching, and SSD offloading changes the scenario entirely). Qwen models tend to have bulky KV-caches for any given token count. But agentic swarms might end up sharing a large cache prefix, so there's scope for potential gains there.
I don't think you can extrapolate that measurement across multiple sequential draws like that. We presumably are comparing against a single trajectory rather than a tree of trajectories. So once we make the wrong choice and step off of the blessed path, we have no way to assign a ranking to the next token; it's error is undefined.
I've seen LLMs self correct in chains of thought ("because of foo and bar, I need to... Wait, bar is not true, so that won't work") so I have to imagine this is a massive overestimate, errors do not necessarily compound.
I would say they do compound until proven otherwise.
Having "Wait, bar is not true, so that won't work" is not necessarily a correction. In fact, the problem is: across a long text it is a correction of a single mistake, but we are talking about thousands here.
But yes, of course that was a rough estimate. But the problem is - we don't really know what we are measuring here. Maybe there's a 2,000,000x difference of intelligence between coding indexes 52 and 50. By some measure that just feels small because that's how we process it akin to audio db.
Regardless the point is KLD and whatever they came up with is not meaningful. And they did not publish comparisons on real benchmarks.
Just at a sniff test level, don't you think that if the quantization resulted in anything like 2M% error in a pretty typical context length, it would be plain as day? You'd do an A/B test and one of them would look like standard generated text and one of them would veer into incoherence? If not - what on would 2M% error even mean then?
> Regardless the point is KLD and whatever they came up with is not meaningful.
I'm not saying you're wrong, I'm just saying this isn't a meaningful metric either, mostly because it is using a different type of error (divergence along a trajectory) than what was actually measured (divergence at a fixed point) and so can't be used for this purpose. It could establish an upper bound but going by your work that upper bound is so high it may as well be infinite. That's somewhat concerning but doesn't necessarily suggest it performs badly in a typical case (which is how I'd interpret an expectation of 2M% error).
It doesn't really matter to this argument, if they are self correcting at all, then we can't assume all errors will permanently injure the trajectory. It's not like dead reckoning or a similar process where there is never an opportunity to reassess. It's more like a long division problem; it is possible to correct errors using in band information, without the external reference dead reckoning would require. (The incidence of false positive self correction does matter to the question of whether the model is actually of comparable quality after the quantization, of course.)
"Qwen3.8-27B-UD-Q8_K_XL.gguf" for instance.
The one downloaded at least 4 days ago is a different thing and is NOT the "Dynamic 3.0" GGUF which I am now downloading, which I presume will have a different sha256 checksum?
The unsloth page says dynamic 3.0 is released "today", but I have an older copy of qwen3.8 27B Q8 which I downloaded, if I remember right, at least 4-5 days ago...
https://huggingface.co/unsloth/Qwen3.8-27B-GGUF
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