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Unsloth Dynamic 3.0 GGUFs
- xlayn 1mo agoHey 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.
- mike-the-brain 1mo agoyou can still have it, no? > 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
- xlayn 1mo agomy bad, you are totally right, thanks!
- gruturo 1mo agoThe 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. Qwaiting for that 3.8-35B-A3B
- danielhanchen 1mo agoHey 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
- xlayn 1mo agoDaniel, question I got the Qwen3.8-27B-UD-Q2_K_XL.gguf from https://huggingface.co/unsloth/Qwen3.8-27B-GGUF?show_file_info=Qwen3.8-27B-UD-Q2_K_XL.gguf 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. I tried https://huggingface.co/mradermacher/Qwen3.8-27B-Uncensored-GGUF https://huggingface.co/mradermacher/Qwen3.8-27B-Uncensored-G... the 8 bit, 6 and 2 bit... the 2 bit almost use the complete KV doing it's thing and didn't loop itself. 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.
- kamranjon 1mo agoAre you using the recommended settings for temperature and such? https://unsloth.ai/docs/models/qwen3.8#recommended-settings https://unsloth.ai/docs/models/qwen3.8#recommended-settings Often times I run into issues like this it’s because I am using settings for a different model or just forget to set them up.
- idonotknowwhy 1mo ago>But I would suggest using UD-IQ3_XXS for 10.9GB for 16GB machines or Q2_K_XL For those of us with a 16GB GPU, how do they compare with ExllamaV4 at 4-bit (4.0bpw)? It looks like that fits in 12.5GB of VRAM since embedding are left in DRAM, Unsloth Studio and other llama.cpp derivatives have to load these weights in VRAM for tied embedding models like Qwen3.8. ExllamaV3 4.0bpw fits in 12.5G of VRAM and beats IQ4_XS according to the measurements here: [turboderp/Qwen3.8-27B-exl3](https://huggingface.co/turboderp/Qwen3.8-27B-exl3 https://huggingface.co/turboderp/Qwen3.8-27B-exl3) But those were compared against UD2.0 I guess. Also plans to support these (SOTA) quants in Unsloth Studio?
- walrus01 1mo agoQ2 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.
- zenoprax 1mo agoSure, but this is true for all lossy compression (audio, images, etc.) Given 16GB of VRAM, what will give me the best experience in OpenCode? Currently using Qwen3.8_Q_3
- walrus01 1mo agoprobably the best experience would be deepseek v4 flash 0731 (it takes about 170GB RAM on the server side for the full thing and RAM reserved for 1M context) via opencode's $10 a month plan until you use that up, it's either Q8 or full precision. Assuming you're ok with doing things with external inference.
- kennywinker 1mo agoWhy would they have listed how much vram they had if they were looking to rent gpu time on someone else's machine?
- walrus01 1mo agoA casual review of my comment history would show that I've been nothing but the biggest proponent of running models locally, and I do so myself a great deal. But one also has to be realistic about the capabilities of what you can do in a 16GB GPU these days. I already said an extra small Q2 quantization was effectively lobotomized so I didn't want to repeat myself. This person has basically run into the limit of state of the art for even a modestly sized local model (this isn't deepseek v4 flash 0731 Q8 which I am running myself locally on a great deal more hardware), this is a 27B dense, but they're just not going to have a good time if they expect good quality results out of a Q2. The choices are either upgrade hardware or pay for external inference.
- mike-the-brain 1mo agoMight 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)?
- smcleod 1mo agoUnsloth 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.
- verdverm 1mo agoQuantization is typically very cheap and fast. It can even be done on hardware that does not fit the model, by processing the weights layer by layer. I use this project: https://github.com/vllm-project/llm-compressor https://github.com/vllm-project/llm-compressor
- kristjansson 1mo agoJust 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. [0]: https://github.com/ggml-org/llama.cpp/blob/master/tools/quantize/README.md https://github.com/ggml-org/llama.cpp/blob/master/tools/quan...
- throwa356262 1mo ago"We also made some smaller UD-1bit quants with UD-IQ1_S being 6.2GB (without MTP) which retain around 72% top-1% accuracy yet being 89% smaller" This is crazy! But has anyone tried these lower quants on real projects?
- kennywinker 1mo agoNot 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
- Aurornis 1mo agoI 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.
- Havoc 1mo agoWhat setup are you using to do said private evaluation? Software wise I mean
- andai 1mo agoI wonder if this would help, or if it solves different kinds of errors. Show HN: Forge – Guardrails take an 8B model from 53% to 99% on agentic tasks https://news.ycombinator.com/item?id=48192383 https://news.ycombinator.com/item?id=48192383
- jadbox 1mo agoThe new IQ4XS has been working pretty well so far on 4090 16gb.
- tetsuo420 1mo agoIt 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?
- spwa4 1mo agoNo MLX versions for 3.8 though.
- lostmsu 1mo agoCool. Now run TerminalHard and compare to unquantized 27B. KLD of 1%, or similar error metric that multiplies, on 10000 tokens would give accumulated error of 2,000,000%
- sosodev 1mo agoThat's not how that works. Selecting a different token is not inherently erroneous. A correct solution can still be found despite divergence.
- lostmsu 1mo agoKLD isn't how that works either. The truth is in the middle and they aren't showing it.
- maxbond 1mo agoI 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.
- lostmsu 1mo agoI 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.
- QuantumNomad_ 1mo agoIs it possible to use a model that needs around 64 GB VRAM if you have four GPUs with 16 GB VRAM each?
- charcircuit 1mo agoOf 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.
- leoooodias 1mo agoYou 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!
- revolvingthrow 1mo agoThe bitrot would be excessive
- BonerWiener 1mo agoIP over Avian Carriers: https://www.rfc-editor.org/info/rfc2549/ https://www.rfc-editor.org/info/rfc2549/
- chuckadams 1mo agoCoincidentally that’s also Google’s new method for distributing Android sources.
- kQq9oHeAz6wLLS 1mo agoThe key to running in lower amounts of VRAM is patience. It'll be slow, but it'll work.
- segbrk 1mo agoYes, but unless they support NVLink (they don't), it's quite slow.
- josh-wrale 1mo agoSidebar: single threaded inference isn’t good enough anymore
- sosodev 1mo agoWhat about do you mean by single threaded? Each token is predicted by using parallel computation on the GPU.
- josh-wrale 1mo agoMultiple agents need tokens. Should optimize for that instead of one agent blocking the others.
- sosodev 1mo agoOne 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.
- zozbot234 1mo agoThe 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.
- redox99 1mo agoI 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.
- josh-wrale 1mo ago
- acuozzo 1mo agoCan this help tiny models like Qwen3.5-0.8B?
- johndough 1mo agoAre 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.
- InvertedRhodium 1mo agoI tested Qwen 3.8 on the Blade CTF last night, it took 3 hours but got the correct answer. I know that didn’t answer your question but I was looking for a test suite and couldn’t find anything. After reading the logs, there is far less doom looping than with 3.6, but whether that’s a one off or not is up for debate. Q4_K_P
- Balinares 1mo agoI anecdotally observed the same. 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.
- danielhanchen 1mo agoWe 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) We do plan to do larger benchmark suites though!
- johndough 1mo agoGreat to hear that you are planning larger benchmarks! I am particularly interested in longer-running tasks with many steps and self-correction. Divergence is fine as long as the model can still solve the task, which Divergence-300 @32 does not measure. The current benchmark suites that frontier AI labs use are probably a good fit, e.g. https://z.ai/blog/glm-5.3#:~:text=Performance%20across%20comparison%20models https://z.ai/blog/glm-5.3#:~:text=Performance%20across%20com... https://www.kimi.ai/ai-models/kimi-k3#:~:text=Performance%20at%20the%20frontier https://www.kimi.ai/ai-models/kimi-k3#:~:text=Performance%20... https://www.anthropic.com/news/claude-opus-5 https://www.anthropic.com/news/claude-opus-5 https://openai.com/index/gpt-5-6/ https://openai.com/index/gpt-5-6/ But guessing from your current benchmarks, I assume that you are severely compute-constrained. What is your time budget?
- Alephinitesimal 1mo agoI 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.
- latentsea 1mo agoQwen3.8-27B has been the turning point for me. It's not as strong as the absolute frontier, but it's the first time I feel local coding models are actually functionally useable as daily drivers.
- Forgeties79 1mo agoMan I am having a hell of a time trying to optimize 3.8 over 3.6. I don’t have a particularly powerful setup but I can usually push 20-30tok/s on 3.6 and I can barely get to 10 on 3.8. Both unsloth same VRAM/RAM distribution more or less. My 3.6 is still producing consistently better results and faster
- Alephinitesimal 1mo agoThat's interesting since both models are dense. I wonder if this is more of an optimization issue with 3.8 rather than something inherent to the architecture.
- johnnyApplePRNG 1mo agoCould have sworn I read these were the same architectures the other day .... 3.6 and 3.8 at this size.
- Forgeties79 1mo agoI’m also not an engineer/coder so it’s equally possible I’m just doing something wrong.
- walrus01 1mo agoIt 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... https://huggingface.co/unsloth/Qwen3.8-27B-GGUF https://huggingface.co/unsloth/Qwen3.8-27B-GGUF
- jlbprof 1mo agoHow can I tell which one I have? I dl'd mine a few days ago.
- walrus01 1mo agoI 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) Qwen3.8-27B-UD-Q8_K_XL-unsloth-dynamic3.0$ openssl dgst -sha256 *.gguf SHA2-256(Qwen3.8-27B-UD-Q8_K_XL.gguf)= af36ecb6b5db1407953345b746c14ac93f0657dda413910b4348683a2d990377 =====separator========= downloaded at least 4 days ago: Qwen3.8-27B-UD-Q8_K_XL-unsloth-original$ openssl dgst -sha256 Qwen3.8-27B-UD-Q8_K_XL.gguf SHA2-256(Qwen3.8-27B-UD-Q8_K_XL.gguf)= af36ecb6b5db1407953345b746c14ac93f0657dda413910b4348683a2d990377 So they're actually the same thing, but the announcement says released today... Please let's not confuse the end users any more than they already are.
- CMay 1mo agoI 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.
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- skyde 1mo agoWould converting those quant to MLX preserve the accuracy/size ? Or this only work with GGUF?
- Systemerror7A69 1mo agoSince 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
- redlinedtm 1mo ago[flagged]
- jjcm 1mo agoNo Dynamic 3.0 NVFP4 quants just yet from the look of it, as a heads up. Would love to see how those perform relative to others on the curve.
- jedbrooke 1mo agohuh, sounds like they’re talking about over fitting and datasets etc, it seems like this is almost more like a fine tune/distill than just a pure quantization
- DisceetPlug 1mo ago[dead]
- DisceetPlug 1mo ago[dead]
- freemindcore 1mo ago[dead]
- ankushdograuk 1mo agoWaiting for MLX version
- jwr 1mo agoThese are very good! I'm hoping for speed improvements because the only problem running the 27B model on my Macbook pro (M4 Max) is the speed: 20 tokens per second. I benchmarked and MTP actually makes things slower, so I disabled MTP altogether. I'm hoping there will be some breakthroughs or optimizations that will allow me to run this at 30-50 tokens per second, which would make a big difference.
- m1keil 1mo agoI have a 36gb M3 Max. I tested it across quite a few different options: llama.cpp, oLMX, ollama with different options. So far ollama managed to be the most performant of them all. I will get 30 to 40 tokes/sec with it when using the -mlx version of Qwen3.8. Whatever the sauce the ollama folks baked into the mlx + MTP mix is currently working the best out of the box.
- jantse 1mo agoThanks for sharing! Did you observe a speed difference between ollamas mlx version and the mlx-community/Qwen3.8-27B-4bit from HF ran with mlx_vlm.generate (with MTP)? Or is it the same?
- deleted 1mo ago[deleted]
- m1keil 1mo agohuh.. I'm a bit of local LLM noob so I wasn't familiar with mlx_vlm. I gave it a shot now: mlx_vlm.generate --model mlx-community/Qwen3.8-27B-4bit --prompt 'give me fizz buzz in rust' --enable-thinking --draft-kind mtp --draft-model mlx-community/Qwen3.8-27B-MTP-4bit --verbose ========== Prompt: 58 tokens, 90.717 tokens-per-sec Generation: 145 tokens, 36.392 tokens-per-sec Peak memory: 17.419 GB Speculative decoding: 2.79 accepted tokens/round (1.79 accepted drafts/round, 89.4% of drafted, avg draft 2.00) over 52 rounds Which is very close to ollama, thank you! I'm not sure if I can get rid of the drafter model, if I understand correctly, the Qwen model already includes a built in draft headers, but just having --draft-kind mtp results in about 17 t/s.
- spider-mario 1mo agoWill this also be applied to older models like Qwen3.6? 35B-A3B still has its uses with its higher speed than the dense 3.8 27B.