Model Description

quantized_by: ubergarm pipeline_tag: text-generation base_model: Qwen/Qwen3.5-122B-A10B basemodelrelation: quantized license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3.5-122B-A10B/blob/main/LICENSE tags:
  • imatrix
  • conversational
  • qwen35moe
  • ik_llama.cpp

ik_llama.cpp imatrix Quantizations of Qwen/Qwen3.5-122B-A10B

The quants in this collection REQUIRE ik_llama.cpp fork to support the ik's latest SOTA quants and optimizations! Do not download these big files and expect them to run on mainline vanilla llama.cpp, ollama, LM Studio, KoboldCpp, etc!

NOTE ikllama.cpp can also run your existing GGUFs from bartowski, unsloth, mradermacher, etc if you want to try it out before downloading my quants. Only a couple quants in this collection are compatible with mainline llamma.cpp/LMStudio/KoboldCPP/etc as mentioned in the specific description, all others require ikllama.cpp.

Some of ik's new quants are supported with Nexesenex/croco.cpp fork of KoboldCPP with Windows builds. Also check for ikllama.cpp windows builds by Thireus here..

These quants provide best in class perplexity 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 on BeaverAI Club Discord and on r/LocalLLaMA for tips and tricks helping each other run, test, and benchmark all the fun new models! Thanks to huggingface for hosting all these big quants!

Finally, I really appreciate the support from aifoundry.org so check out their open source RISC-V based solutions!

Quant Collection

Perplexity computed against wiki.test.raw. (lower is "better")

!Perplexity Chart

These two are just test quants for baseline perplexity comparison and not available for download here:

  • BF16 227.525 GiB (16.005 BPW)

- PPL over 580 chunks for n_ctx=512 = 4.8159 +/- 0.02839
  • Q8_0 120.942 GiB (8.508 BPW)

- PPL over 580 chunks for n_ctx=512 = 4.8196 +/- 0.02841

NOTE: The first split file is much smaller on purpose to only contain metadata, its fine!

IQ4_KSS 61.219 GiB (4.306 BPW)

PPL over 580 chunks for n_ctx=512 = 4.8741 +/- 0.02879

👈 Secret Recipe

#!/usr/bin/env bash


custom="
60 Repeating Layers [0-59]



Gated Attention/Delta Net [Blended 0-59]

blk\..*\.attngate\.weight=q80
blk\..*\.attnqkv\.weight=q80
blk\..*\.attnoutput\.weight=q80
blk\..*\.attnq\.weight=q80
blk\..*\.attnk\.weight=q80
blk\..*\.attnv\.weight=q80
blk\..*\.ssmalpha\.weight=q80
blk\..*\.ssmbeta\.weight=q80
blk\..*\.ssmout\.weight=q80


Shared Expert Layers [0-59]

blk\..*\.ffndownshexp\.weight=q8_0
blk\..*\.ffn(gate|up)shexp\.weight=q8_0


Routed Experts Layers [0-59]

blk\..*\.ffndownexps\.weight=iq4_ks
blk\..*\.ffn(gate|up)exps\.weight=iq4_kss


Non-Repeating Layers

tokenembd\.weight=iq6k
output\.weight=iq6_k
"


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



numactl -N ${SOCKET} -m ${SOCKET} \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/data/models/ubergarm/Qwen3.5-122B-A10B-GGUF/imatrix-Qwen3.5-122B-A10B-BF16.dat \
    /mnt/data/models/ubergarm/Qwen3.5-122B-A10B-GGUF/Qwen3.5-122B-A10B-BF16-00001-of-00005.gguf \
    /mnt/data/models/ubergarm/Qwen3.5-122B-A10B-GGUF/Qwen3.5-122B-A10B-IQ4_KSS.gguf \
    IQ4_KSS \
    128

IQ2_KL 43.319 GiB (3.047 BPW)

PPL over 580 chunks for n_ctx=512 = 5.1012 +/- 0.03038

👈 Secret Recipe

#!/usr/bin/env bash


custom="
60 Repeating Layers [0-59]



Gated Attention/Delta Net [Blended 0-59]

blk\..*\.attngate\.weight=iq6k
blk\..*\.attnqkv\.weight=iq6k
blk\..*\.attnoutput\.weight=iq6k
blk\..*\.attnq\.weight=iq6k
blk\..*\.attnk\.weight=iq6k
blk\..*\.attnv\.weight=iq6k
blk\..*\.ssmalpha\.weight=iq6k
blk\..*\.ssmbeta\.weight=iq6k
blk\..*\.ssmout\.weight=iq6k


Shared Expert Layers [0-59]

blk\..*\.ffndownshexp\.weight=iq6_k
blk\..*\.ffn(gate|up)shexp\.weight=iq6_k


Routed Experts Layers [0-59]

blk\..*\.ffndownexps\.weight=iq3_ks
blk\..*\.ffn(gate|up)exps\.weight=iq2_kl


Non-Repeating Layers

tokenembd\.weight=iq4k
output\.weight=iq6_k
"


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



#--dry-run \
numactl -N ${SOCKET} -m ${SOCKET} \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/data/models/ubergarm/Qwen3.5-122B-A10B-GGUF/imatrix-Qwen3.5-122B-A10B-BF16.dat \
    /mnt/data/models/ubergarm/Qwen3.5-122B-A10B-GGUF/Qwen3.5-122B-A10B-BF16-00001-of-00005.gguf \
    /mnt/data/models/ubergarm/Qwen3.5-122B-A10B-GGUF/Qwen3.5-122B-A10B-IQ2_KL.gguf \
    IQ2_KL \
    128

smol-IQ2_KS 35.319 GiB (2.485 BPW)

PPL over 580 chunks for n_ctx=512 = 5.4614 +/- 0.03292

👈 Secret Recipe

#!/usr/bin/env bash


custom="
60 Repeating Layers [0-59]



Gated Attention/Delta Net [Blended 0-59]

blk\..*\.attngate\.weight=q80
blk\..*\.attnqkv\.weight=q80
blk\..*\.attnoutput\.weight=q80
blk\..*\.attnq\.weight=q80
blk\..*\.attnk\.weight=q80
blk\..*\.attnv\.weight=q80
blk\..*\.ssmalpha\.weight=q80
blk\..*\.ssmbeta\.weight=q80
blk\..*\.ssmout\.weight=q80


Shared Expert Layers [0-59]

blk\..*\.ffndownshexp\.weight=q8_0
blk\..*\.ffn(gate|up)shexp\.weight=q8_0


Routed Experts Layers [0-59]

blk\..*\.ffndownexps\.weight=iq2_ks
blk\..*\.ffn(gate|up)exps\.weight=iq2_ks


Non-Repeating Layers

tokenembd\.weight=iq4k
output\.weight=iq6_k
"


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



numactl -N ${SOCKET} -m ${SOCKET} \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/data/models/ubergarm/Qwen3.5-122B-A10B-GGUF/imatrix-Qwen3.5-122B-A10B-BF16.dat \
    /mnt/data/models/ubergarm/Qwen3.5-122B-A10B-GGUF/Qwen3.5-122B-A10B-BF16-00001-of-00005.gguf \
    /mnt/data/models/ubergarm/Qwen3.5-122B-A10B-GGUF/Qwen3.5-122B-A10B-smol-IQ2_KS.gguf \
    IQ2_KS \
    128

IQ1_KT 30.217 GiB (2.126 BPW)

PPL over 580 chunks for n_ctx=512 = 5.7763 +/- 0.03535

👈 Secret Recipe

#!/usr/bin/env bash


custom="
60 Repeating Layers [0-59]



Gated Attention/Delta Net [Blended 0-59]

blk\..*\.attngate\.weight=iq6k
blk\..*\.attnqkv\.weight=iq6k
blk\..*\.attnoutput\.weight=iq6k
blk\..*\.attnq\.weight=iq6k
blk\..*\.attnk\.weight=iq6k
blk\..*\.attnv\.weight=iq6k
blk\..*\.ssmalpha\.weight=iq6k
blk\..*\.ssmbeta\.weight=iq6k
blk\..*\.ssmout\.weight=iq6k


Shared Expert Layers [0-59]

blk\..*\.ffndownshexp\.weight=iq6_k
blk\..*\.ffn(gate|up)shexp\.weight=iq6_k


Routed Experts Layers [0-59]

blk\..*\.ffndownexps\.weight=iq2_kt
blk\..*\.ffn(gate|up)exps\.weight=iq1_kt


Non-Repeating Layers

tokenembd\.weight=iq4k
output\.weight=iq6_k
"


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



#--dry-run \
numactl -N ${SOCKET} -m ${SOCKET} \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/data/models/ubergarm/Qwen3.5-122B-A10B-GGUF/imatrix-Qwen3.5-122B-A10B-BF16.dat \
    /mnt/data/models/ubergarm/Qwen3.5-122B-A10B-GGUF/Qwen3.5-122B-A10B-BF16-00001-of-00005.gguf \
    /mnt/data/models/ubergarm/Qwen3.5-122B-A10B-GGUF/Qwen3.5-122B-A10B-IQ1_KT.gguf \
    IQ1_KT \
    128

Quick Start

# Clone and checkout
$ git clone https://github.com/ikawrakow/ik_llama.cpp
$ cd ik_llama.cpp


Build for hybrid CPU+CUDA

$ cmake -B build -DCMAKEBUILDTYPE=Release -DGGML_CUDA=ON
$ cmake --build build --config Release -j $(nproc)


Download Desired Quants

$ pip install huggingface_hub
$ hf download --local-dir ./ --include=smol-IQ2_KS/*.gguf ubergarm/Qwen3.5-122B-A10B-GGUF


Full GPU Offload

./build/bin/llama-server \
  --model "$model" \
  --alias Qwen3.5-122B-A10B \
  -c 262144 \
  -fa on \
  -ger \
  --merge-qkv \
  -sm graph \
  -ngl 99 \
  -ub 4096 -b 4096 \
  --parallel 2 \
  --threads 1 \
  --host 127.0.0.1 \
  --port 8080 \
  --jinja \
  --no-mmap


Hybrid CPU+GPU Offload

echo TODO or see other recent modelcards for examples running Qwen3.5


CPU-Only Inference

numactl -N "$SOCKET" -m "$SOCKET" \
./build/bin/llama-server \
    --model "$model"\
    --alias ubergarm/Qwen3.5-122B-A10B \
    --ctx-size 65536 \
    -ctk q80 -ctv q80 \
    --parallel 1 \
    --threads 96 \
    --threads-batch 128 \
    --numa numactl \
    --host 127.0.0.1 \
    --port 8080 \
    --no-mmap \
    --jinja

If you're using chat completions endpoint, you can disable thinking with --chat-template-kwargs '{"enable_thinking": false }'.

References

GGUF File List

📁 Filename 📦 Size ⚡ Download
Qwen3.5-122B-A10B-IQ1_KT.gguf
Recommended LFS
30.23 GB Download
Qwen3.5-122B-A10B-IQ2_KL.gguf
LFS Q2
43.33 GB Download
Qwen3.5-122B-A10B-IQ4_KSS.gguf
LFS Q4
61.23 GB Download
Qwen3.5-122B-A10B-smol-IQ2_KS.gguf
LFS Q2
35.33 GB Download