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Qwen3-235B-A22B-GGUF

Text Generation
GGUF
imatrix
qwen3_moe
conversational
ik_llama.cpp
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metadata
quantized_by: ubergarm
pipeline_tag: text-generation
base_model: Qwen/Qwen3-235B-A22B
license: mit
base_model_relation: quantized
tags:
  - imatrix
  - qwen3_moe
  - conversational
  - ik_llama.cpp

ik_llama.cpp imatrix Quantizations of Qwen/Qwen3-235B-A22B

This quant collection REQUIRES ik_llama.cpp fork to support advanced non-linear SotA quants. Do not download these big files and expect them to run on mainline vanilla llama.cpp, ollama, LM Studio, KoboldCpp, etc!

These quants provide best in class quality for the given memory footprint.

Big Thanks

Shout out to Wendell and the Level1Techs crew, the community Forums, YouTube Channel! BIG thanks for providing BIG hardware expertise and access to run these experiments and make these great quants available to the community!!!

Also thanks to all the folks in the quanting and inferencing community here and on r/LocalLLaMA for tips and tricks helping each other run all the fun new models!

Excited to share and learn together. Thanks!

Quant Collection

So far these are my best recipes offering the great quality in good memory footprint breakpoints.

ubergarm/Qwen3-235B-A22B-mix-IQ3_K.gguf

This quant is designed to run at max speed with just under ~110GiB (V)RAM combinations e.g. 24GB VRAM + 96GB RAM (perfect for AM5 or LGA 1700 gamer rigs with 2x48GiB DDR5 DIMMs for max performance). This will allow for -rtr run-time repacking for maximum CPU throughput. You can still omit -rtr and use default mmap() behavior to run in less RAM at a penalty to speed. Or you can also "offline repack" to fit your exact setup and get the best of both worlds with quicker startup with mmap() and max CPU throughput.

106.830 GiB (3.903 BPW)

  f32:  471 tensors
 q8_0:    2 tensors
iq3_k:  188 tensors
iq4_k:   94 tensors
iq6_k:  376 tensors

Final estimate: PPL = 5.4403 +/- 0.03421 (compare to Q8_0 at 5.3141 +/- 0.03321) (TODO: more benchmarking)

Quick Start

ik_llama.cpp API server for GPU inferencing 

# This example for 24GB VRAM + 96 GB RAM + 16 physical core CPU
./build/bin/llama-server
  --model ubergarm/Qwen3-235B-A22B-GGUF/Qwen3-235B-A22B-mix-IQ3_K.gguf \
  -fa \
  -ctk q8_0 -ctv q8_0 \
  -c 32768 \
  -fmoe \
  -amb 512 \
  -rtr \
  -ot blk\.[0-9]\.ffn.*=CUDA0 \
  -ot blk\.1[0-2]\.ffn.*=CUDA0 \
  -ot exps=CPU \
  -ngl 99 \
  --threads 16
  --host 127.0.0.1 \
  --port 8080

If you want more context and/or less VRAM usage, you can try:

  • Smaller KV Cache quantization -ctk q4_0 -ctv q4_0

Model Architechture

There are 94 repeating layers/blocks with the unquantized bf16 version being 448501.04 MB total.

Tensor Dimension Data Type Size
token_embd.weight [ 4096, 151936, 1, 1] bf16 1187.00 MiB
blk.1.attn_k_norm.weight [ 128, 1, 1, 1] f32 0.000 MiB
blk.1.attn_q_norm.weight [ 128, 1, 1, 1] f32 0.000 MiB
blk.1.attn_norm.weight [ 4096, 1, 1, 1] f32 0.016 MiB
blk.1.ffn_gate_inp.weight [ 4096, 128, 1, 1] f32 2.000 MiB
blk.1.ffn_norm.weight [ 4096, 1, 1, 1] f32 0.016 MiB
blk.1.attn_k.weight [ 4096, 512, 1, 1] bf16 4.00 MiB
blk.1.attn_q.weight [ 4096, 8192, 1, 1] bf16 64.00 MiB
blk.1.attn_v.weight [ 4096, 512, 1, 1] bf16 4.00 MiB
blk.1.attn_output.weight [ 8192, 4096, 1, 1] bf16 64.00 MiB
blk.1.ffn_down_exps.weight [ 1536, 4096, 128, 1] bf16 1536.00 MiB
blk.1.ffn_gate_exps.weight [ 4096, 1536, 128, 1] bf16 1536.00 MiB
blk.1.ffn_up_exps.weight [ 4096, 1536, 128, 1] bf16 1536.00 MiB
output.weight [ 4096, 151936, 1, 1] bf16 1187.00 MiB
output.norm_weight [ 4096, 1, 1, 1] f32 0.016MiB

Quantization

👈Secret Recipe 
#!/usr/bin/env bash

custom="
# Attention
blk\..*\.attn_k.*=iq6_k
blk\..*\.attn_q.*=iq6_k
blk\..*\.attn_v.*=iq6_k
blk\..*\.attn_output.*=iq6_k

# Token Embedding (put these second so attn_output regex doesn't become q8_0)
token_embd\.weight=q8_0
output\.weight=q8_0

# Experts
blk\..*\.ffn_down_exps\.weight=iq4_k
blk\..*\.ffn_(gate|up)_exps\.weight=iq3_k
"

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

    #--token-embedding-type q8_0 \
    #--output-tensor-type q8_0 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-235B-A22B-GGUF/imatrix-Qwen3-235B-A22B.dat \
    /mnt/raid/models/Qwen/Qwen3-235B-A22B/Qwen3-235B-A22B-BF16-00001-of-00011.gguf \
    /mnt/raid/models/ubergarm/Qwen3-235B-A22B-GGUF/Qwen3-235B-A22B-mix-IQ3_K.gguf \
    IQ3_K \
    24

Discussion

TODO: Discuss some about comparing quants e.g. bartowski, unsloth, and mradermacher including "quality" and "speed".

References