For those interested, made some Dynamic Unsloth GGUFs for local deployment at https://huggingface.co/unsloth/Qwen3-Coder-Next-GGUF and made a guide on using Claude Code / Codex locally: https://unsloth.ai/docs/models/qwen3-coder-next
For those interested, made some Dynamic Unsloth GGUFs for local deployment at https://huggingface.co/unsloth/Qwen3-Coder-Next-GGUF and made a guide on using Claude Code / Codex locally: https://unsloth.ai/docs/models/qwen3-coder-next
Nice! Getting ~39 tok/s @ ~60% GPU util. (~170W out of 303W per nvtop).
System info:
llama.cpp command-line:Super cool! Also with `--fit on` you don't need `--ctx-size 32768` technically anymore - llama-server will auto determine the max context size!
What am I missing here? I thought this model needs 46GB of unified memory for 4-bit quant. Radeon RX 7900 XTX has 24GB of memory right? Hoping to get some insight, thanks in advance!
MoEs can be efficiently split between dense weights (attention/KV/etc) and sparse (MoE) weights. By running the dense weights on the GPU and offloading the sparse weights to slower CPU RAM, you can still get surprisingly decent performance out of a lot of MoEs.
Not as good as running the entire thing on the GPU, of course.
Hi Daniel, I've been using some of your models on my Framework Desktop at home. Thanks for all that you do.
Asking from a place of pure ignorance here, because I don't see the answer on HF or in your docs: Why would I (or anyone) want to run this instead of Qwen3's own GGUFs?
Thanks! Oh Qwen3's own GGUFs also works, but ours are dynamically quantized and calibrated with a reasonably large diverse dataset, whilst Qwen's ones are not - see https://unsloth.ai/docs/basics/unsloth-dynamic-2.0-ggufs
I've read that page before and although it all certainly sounds very impressive, I'm not an AI researcher. What's the actual goal of dynamic quantization? Does it make the model more accurate? Faster? Smaller?
Still hoping IQuest-Coder gets the same treatment :)
What is the difference between the UD and non-UD files?
UD stands for "Unsloth-Dynamic" which upcasts important layers to higher bits. Non UD is just standard llama.cpp quants. Both still use our calibration dataset.
Please consider authoring a single, straightforward introductory-level page somewhere that explains what all the filename components mean, and who should use which variants.
The green/yellow/red indicators for different levels of hardware support are really helpful, but far from enough IMO.
Oh good idea! In general UD-Q4_K_XL (Unsloth Dynamic 4bits Extra Large) is what I generally recommend for most hardware - MXFP4_MOE is also ok
Is there some indication on how the different bit quantization affect performance? IE I have a 5090 + 96GB so I want to get the best possible model but I don't care about getting 2% better perf if I only get 5 tok/s.
It takes download time + 1 minute to test speed yourself, you can try different quants, it's hard to write down a table because it depends on your system ie. ram clock etc. if you go out of gpu.
I guess it would make sense to have something like max context size/quants that fit fully on common configs with gpus, dual gpus, unified ram on mac etc.
Testing speed is easy yes, I'm mostly wondering about the quality difference between Q6 vs Q8_K_XL for example.
I haven't done benchmarking yet (plan to do them), but it should be similar to our post on DeepSeek-V3.1 Dynamic GGUFs: https://unsloth.ai/docs/basics/unsloth-dynamic-2.0-ggufs
The green/yellow/red indicators are based on what you set for your hardware on huggingface.
What is your definition of "important" in this context?
Oh we wrote about it here: https://unsloth.ai/docs/basics/unsloth-dynamic-2.0-ggufs
How did you do it so fast?
Great work as always btw!
Thanks! :) We're early access partners with them!
Good results with your Q8_0 version on 96GB RTX 6000 Blackwell. It one-shotted the Flappy Bird game and also wrote a good Wordle clone in four shots, all at over 60 tps. Thanks!
Is your Q8_0 file the same as the one hosted directly on the Qwen GGUF page?
Nice! Yes Q8_0 is similar - the others are different since they use a calibration dataset.