ubergarm/Qwen3.5-27B-GGUF

ik_llama.cpp imatrix Quantizations of Qwen/Qwen3.5-27B

NOTE ik_llama.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 ik_llama.cpp.

Some of ik's new quants are supported with Nexesenex/croco.cpp fork of KoboldCPP with Windows builds. Also check for ik_llama.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")

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

• BF16 50.103 GiB (16.002 BPW)
  • PPL over 580 chunks for n_ctx=512 = 6.8733 +/- 0.04478
• Q8_0 26.622 GiB (8.502 BPW)
  • PPL over 580 chunks for n_ctx=512 = 6.8689 +/- 0.04474

Just a rough KLD graph this time because the data was not so well behaved as the usual MoE models so harder to draw clear conclusions. See discussions for more information and benchmarking.

NOTE: If the models are split, the first file is much smaller and only contains metadata, that is on purpose, its fine!

IQ5_KS 18.532 GiB (5.919 BPW)

Final estimate: PPL over 580 chunks for n_ctx=512 = 6.8900 +/- 0.04490

This ik_llama.cpp exclusive quant is likely among the best quality available for 24GB full offload.

👈 Secret Recipe

#!/usr/bin/env bash

custom="
# 64 Repeating Layers [0-63]

## Gated Attention/Delta Net [Blended 0-63]
blk\..*\.attn_gate\.weight=q6_0
blk\..*\.attn_qkv\.weight=q6_0
blk\..*\.attn_output\.weight=q6_0
blk\..*\.attn_q\.weight=q6_0
blk\..*\.attn_k\.weight=q6_0
blk\..*\.attn_v\.weight=q6_0
blk\..*\.ssm_alpha\.weight=q8_0
blk\..*\.ssm_beta\.weight=q8_0
blk\..*\.ssm_out\.weight=q8_0

# Dense Layers [0-63]
blk\.[0-4]\.ffn_down_exps\.weight=q6_0
blk\..*\.ffn_down\.weight=iq5_ks
blk\..*\.ffn_(gate|up)\.weight=iq5_ks

# Non-Repeating Layers
token_embd\.weight=q6_0
output\.weight=q8_0
"

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/Qwen3.5-27B-GGUF/imatrix-Qwen3.5-27B-BF16.dat \
    /mnt/data/models/ubergarm/Qwen3.5-27B-GGUF/Qwen3.5-27B-BF16-00001-of-00002.gguf \
    /mnt/data/models/ubergarm/Qwen3.5-27B-GGUF/Qwen3.5-27B-IQ5_KS.gguf \
    IQ5_KS \
    128

smol-IQ4_NL 15.405 GiB (4.920 BPW)

PPL over 580 chunks for n_ctx=512 = 6.8753 +/- 0.04471

This mainline compatible custom mix using quantization types hopefully optimized for Vulkan/ROCm (and possibly Mac)?

👈 Secret Recipe

#!/usr/bin/env bash

custom="
# 64 Repeating Layers [0-63]

## Gated Attention/Delta Net [Blended 0-63]
blk\..*\.attn_gate\.weight=iq4_nl
blk\..*\.attn_qkv\.weight=iq4_nl
blk\..*\.attn_output\.weight=iq4_nl
blk\..*\.attn_q\.weight=iq4_nl
blk\..*\.attn_k\.weight=iq4_nl
blk\..*\.attn_v\.weight=iq4_nl
blk\..*\.ssm_alpha\.weight=q8_0
blk\..*\.ssm_beta\.weight=q8_0
blk\..*\.ssm_out\.weight=q8_0

# Dense Layers [0-63]
blk\..*\.ffn_down\.weight=iq4_nl
blk\..*\.ffn_(gate|up)\.weight=iq4_nl

# Non-Repeating Layers
token_embd\.weight=iq4_nl
output\.weight=q8_0
"

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/Qwen3.5-27B-GGUF/imatrix-Qwen3.5-27B-BF16.dat \
    /mnt/data/models/ubergarm/Qwen3.5-27B-GGUF/Qwen3.5-27B-BF16-00001-of-00002.gguf \
    /mnt/data/models/ubergarm/Qwen3.5-27B-GGUF/Qwen3.5-27B-smol-IQ4_NL.gguf \
    IQ4_NL \
    128

References

• ik_llama.cpp
• ubergarm on quantizing LLMs and tuning GPUs with aifoundry.org
• ubergarm-imatrix-calibration-corpus-v02.txt
• Getting Started Guide (out of date)
• Quant Cookers Guide (out of date)
• high quality imatrix MoE optimized mainline llama.cpp quants AesSedai