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Can you describe what is this slightly different approach and why it should work on all models?

Nice! Your stuff ran LLMs extremely well on < $500 boxes (24-32GB ram) with iGPUS before this update.

I’m eager to try it out, especially if 16GB is viable now.

Yep. These Mixture of Experts models are well suited for paging in only the relevant data for a certain task https://huggingface.co/blog/moe

There's some experiments of just removing or merging experts post training to shrink models even more https://bknyaz.github.io/blog/2026/moe/

llama.cpp is designed for partial offloading, the most important part of the model will be loaded into the GPU and the rest on system ram. I run 500B+ models such as DeepSeek/KimiK2.5/GLM-5 without having that much GPU vram.

The A3B part in the name stands for `Active 3B`, so for the inference jobs a core 3B is used in conjunction with another subpart of the model, based on the task (MoE, mixture of experts). If you use these models mostly for related/similar tasks, that means you can make do with a lot less than the 35B params in active RAM. These models are therefore also sometimes called sparse models.

This is why they say "A3B" meaning only 3B is active at a time, limiting VRAM usage.

32GB vram is more than enough for Qwen 3.5 35b

You can just load the Q4_K_XL model like normal, and put all tensors on GPU without any -ot or --cpu-moe flags.

If you need a massive context for some reason where model+kv cache won't fit in 32gb, then use -ot to move the ffn moe experts for 1-2 layers into RAM. You'll get a speed hit (due to loading params from slower RAM instead of fast VRAM) but it'll work.

1 4090, Qwen3.5-35B-A3B-UD-MXFP4_MOE, 64k context, 122 t/s. Llama.cpp

Yes, but make sure you grab the latest llama.cpp release

New model archs usually involve code changes.

You would need the Dynamic 2.0 GGUF as discussed in the article.

But mmmmmm, Q8_K_XL looks mighty nice.

No this is false - unsure if you saw our new blog - https://unsloth.ai/docs/models/qwen3.5/gguf-benchmarks which shows SOTA on nearly all bits, and we shared all our research as well

I'm aware of that, but that's not the link of the post. The post is linking to their UD 2.0 quants from a few months back.

Also, the benchmarks are because they messed up the first version of their Qwen 3.5 XL quants by quanting some tensors to mxfp4 that should have been in higher quality, and this is their bugfix. The post literally starts out with "We updated Qwen3.5-35B Unsloth Dynamic quants being SOTA on nearly all bits" without explaining WHY they needed to update from the original version.

Looking at their benchmarks there doesn't appear to be meaningful difference between their quants and bartowsky quants.