Qwen3.8-27B-GGUF

ik_llama.cpp imatrix Quantizations of Qwen/Qwen3.8-27B

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 quantizing 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 50.894 GiB (16.002 BPW)
  • PPL over 580 chunks for n_ctx=512 = 6.9540 +/- 0.04500
• Q8_0 27.042 GiB (8.502 BPW)
  • PPL over 580 chunks for n_ctx=512 = 6.9554 +/- 0.04500

MTP IQ4_KS 15.749 GiB (4.731 BPW)

PPL over 580 chunks for n_ctx=512 = 6.9938 +/- 0.04527

New improved recipe! Size includes extra iq4_ks mtp head so no need for -mtprot iq4_ks now.

#!/usr/bin/env bash

custom="
# 64 Repeating Layers [0-63] + blk.64 MTP/nextn tensors

## MTP/nextn tensors
blk\.64\..*\.weight=iq4_ks

## Gated Attention/Delta Net [Blended 0-63]
blk\..*\.attn_gate\.weight=iq4_ks
blk\..*\.attn_qkv\.weight=iq4_ks
blk\..*\.attn_output\.weight=iq4_ks
blk\..*\.attn_q\.weight=iq4_ks
blk\..*\.attn_k\.weight=iq4_ks
blk\..*\.attn_v\.weight=iq4_ks
blk\..*\.ssm_alpha\.weight=q8_0
blk\..*\.ssm_beta\.weight=q8_0
blk\..*\.ssm_out\.weight=q6_0

# Dense Layers [0-63]
blk\..*\.ffn_down\.weight=iq4_ks
blk\..*\.ffn_(gate|up)\.weight=iq4_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 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --extra-output-tensor iq4_ks \
    --imatrix /mnt/data/models/ubergarm/Qwen3.8-27B-GGUF/imatrix-Qwen3.8-27B-BF16.dat \
    /mnt/data/models/ubergarm/Qwen3.8-27B-GGUF-mtp/Qwen3.8-27B-BF16-00001-of-00002.gguf \
    /mnt/data/models/ubergarm/Qwen3.8-27B-GGUF-mtp/Qwen3.8-27B-MTP-IQ4_KS.gguf \
    IQ4_KS \
    16

Quick Start

git clone https://github.com/ikawrakow/ik_llama.cpp.git
cd ik_llama.cpp

cmake -B build -DCMAKE_BUILD_TYPE=Release -DGGML_CUDA=ON -DGGML_CUDA_F16=ON
cmake --build build --config Release -j $(nproc)

# wget https://huggingface.co/ubergarm/Qwen3.8-27B-GGUF/resolve/main/Qwen3.8-27B-MTP-IQ4_KS.gguf
model=/mnt/ai/models/ubergarm/Qwen3.8-27B-GGUF/Qwen3.8-27B-MTP-IQ4_KS.gguf
# wget wget https://huggingface.co/ggml-org/Qwen3.8-27B-GGUF/resolve/main/mmproj-Qwen3.8-27B-Q8_0.gguf
mmproj=/mnt/ai/models/ubergarm/Qwen3.8-27B-GGUF/mmproj-Qwen3.8-27B-Q8_0.gguf

CUDA_VISIBLE_DEVICES="0" \
./build/bin/llama-server \
  --model "$model" \
  --alias "Qwen3.8-27B" \
  -c 131072 \
  -ctk q8_0 -ctv q8_0 \
  -ctkd q8_0 -ctvd q8_0 \
  --merge-qkv \
  -muge \
  -ngl 99 \
  -t 1 \
  -tb 1 \
  -tm 16 \
  --host 127.0.0.1 \
  --port 8080 \
  --parallel 1 \
  --jinja \
  --ctx-checkpoints 32 \
  -cram 32768 \
  --spec-type mtp:n_max=4,p_min=0.0 \
  --no-mmproj-offload \
  --mmproj "$mmproj"

This command keeps the mmproj on CPU/RAM and is set to 16 physical cores currently, reduce -tm 8 etc if you have less cores.

If you have multiple CUDA GPUs add -sm graph.

Optional custom chat templates with --chat-template-file ./chat_template.jinja \ which may help performance especially with some harness combinations. Pick one of these or bring your own custom chat template as desired.

# wget https://huggingface.co/peculiar-ragdoll/Qwen-Sharp-Chat-Templates/raw/main/chat_template.jinja
# wget https://huggingface.co/froggeric/Qwen-Fixed-Chat-Templates/raw/main/chat_template.jinja

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