The hybrid quant employs different quantization levels on a per layer basis to increase flexibility of trading off performance vs file size. Less parameter bits are used at deep layers and more bits at cortex layers to simultaneously optimize quantized size and model performance. For this file the layer quants are as follows:

   LAYER_TYPES='[
   [0 ,"Q4_K_M"],[1 ,"Q4_K_M"],[2 ,"Q4_K_S"],[3 ,"Q3_K_L"],[4 ,"Q3_K_M"],[5 ,"Q3_K_M"],[6 ,"Q3_K_M"],[7 ,"Q3_K_M"],
   [8 ,"Q3_K_L"],[9 ,"Q3_K_M"],[10,"Q3_K_L"],[11,"Q3_K_M"],[12,"Q3_K_L"],[13,"Q3_K_M"],[14,"Q3_K_L"],[15,"Q3_K_M"],
   [16,"Q3_K_L"],[17,"Q3_K_M"],[18,"Q3_K_L"],[19,"Q3_K_M"],[20,"Q3_K_L"],[21,"Q3_K_L"],[22,"Q3_K_L"],[23,"Q3_K_L"],
   [24,"Q3_K_L"],[25,"Q3_K_L"],[26,"Q3_K_L"],[27,"Q3_K_L"],[28,"Q4_K_S"],[29,"Q3_K_L"],[30,"Q4_K_S"],[31,"Q3_K_L"],
   [32,"Q4_K_S"],[33,"Q3_K_L"],[34,"Q4_K_S"],[35,"Q3_K_L"],[36,"Q4_K_S"],[37,"Q4_K_S"],[38,"Q4_K_S"],[39,"Q4_K_S"],
   [40,"Q4_K_S"],[41,"Q4_K_S"],[42,"Q4_K_S"],[43,"Q4_K_S"],[44,"Q4_K_M"],[45,"Q5_K_S"],[46,"Q5_K_M"],[47,"Q6_K"  ]
   ]'
   FLAGS="--token-embedding-type Q6_K --output-tensor-type Q6_K --layer-types-high"

The layer quants were optimized for good performance on the non thinking variant of 30B A3B 2507 and reused verbatim on the thinking version. Tests show it performs well, about grade B, on a set of curated test promps, even getting one IQ-test-like problem right that virtually every other tested model (including strong ones like QwQ and GLM Z1) fails while tripping up on some other easier problems. Nonetheless the evals show a pretty solid performance across a wide range of diverse problems.

Comparison:

Quant	size	PPL	Comment
IQ4_XS	16.6e9	7.4	default embed and output, unstable with greedy sampling
Q4_K_H	16.8e9	7.5	Q6_K embed Q6_K output, stable with greedy sampling

Usage:

Compared to the first Qwen3-30B-A3B this model changes:

Bigger native context of 256k extendable to 1M with yarn
Only thinking mode is available. It is a dedicated RL trained thinking model with think block header similar to QwQ and think mode of original Qwen3 series. Just like QwQ, overthinking is baked into the model training. It might be possible to nudge the model to use less overthinking in the prompt but this was not tested. GLM Z1 9B is an example of a model which does not overthink while still being able to solve some pretty tricky problems correctly.

This moe model can be efficiently run by offloading expert tensors to CPU via -ot exps=CPU to open up very large context space. The smaller size of the optimally quantized parameters will give an effective boost in CPU processing speed due to reducing the memory BW needed to repeatedly copy them from main memory to SIMD regs. It can also run fully offloaded on GPU via RPC or high VRAM GPU.

The recommended speculator for the model is Qwen3-0.6B if the inference platform can support vocabulary translation between draft and target. Approximate performance using 4070 GPU and a 9900k CPU with a downstream speculator used with llama.cpp:

Config	block 4 think mode gen speed
2 4070, RPC, fully offloaded to GPU	42 t/s
1 4070, -ot exps=CPU, CPU=9900k	18 t/s

Benchmarks:

Evals for the model will eventually be given here: https://huggingface.co/spaces/steampunque/benchlm.

Download the file from below:

Link	Type	Size/e9 B	Notes
Qwen3-30B-A3B-Thinking-2507.Q4_K_H.gguf	Q4_K_H	16.8e9 B	~IQ4_XS size

A discussion thread about the hybrid layer quant approach can be found here on the llama.cpp git repository:

https://github.com/ggml-org/llama.cpp/discussions/13040

steampunque/Qwen3-30B-A3B-Thinking-2507-MP-GGUF

Mixed Precision GGUF layer quantization of Qwen3-30B-A3B-Thinking-2507 by Qwen

Download the file from below:

steampunque/
Qwen3-30B-A3B-Thinking-2507-MP-GGUF