Comment by danielhanchen 11 hours ago
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
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.
Super cool! Also with `--fit on` you don't need `--ctx-size 32768` technically anymore - llama-server will auto determine the max context size!
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
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
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
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.
Nice! Getting ~39 tok/s @ ~60% GPU util. (~170W out of 303W per nvtop).
System info:
llama.cpp command-line: