This quant collection REQUIRES 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 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.

Some of ik's new quants are supported with Nexesenex/croco.cpp fork of KoboldCPP. Precompiled binaries compatible with windows available on CUDA 12.9.

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!

Quant Collection

Perplexity computed against wiki.test.raw. This first one is just test quants for baseline perplexity comparison:

Q8_0 475.297 GiB (8.503 BPW)
- Final estimate: PPL = 5.0975 +/- 0.03261

`IQ5_K` 329.804 GiB (5.900 BPW)

Final estimate: PPL = 5.1073 +/- 0.03268

👈 Secret Recipe

bash

#!/usr/bin/env bash

# Repeating Layers [0-61]

custom="
# Attention
blk\..*\.attn_q.*=iq6_k
blk\..*\.attn_k.*=q8_0
blk\..*\.attn_v.*=q8_0
blk\..*\.attn_output.*=iq6_k

# Routed Experts
blk\..*\.ffn_down_exps\.weight=iq6_k
blk\..*\.ffn_(gate|up)_exps\.weight=iq5_k

# Non-Repeating Layers
token_embd\.weight=iq6_k
output\.weight=iq6_k
"

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

numactl -N 0 -m 0 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/imatrix-Qwen3-Coder-480B-A35B-Instruct-Q8_0.dat \
    /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/Qwen3-Coder-480B-A35B-Instruct-BF16-00001-of-00021.gguf \
    /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/Qwen3-Coder-480B-A35B-Instruct-IQ5_K.gguf \
    IQ5_K \
    192

`IQ4_K` 273.041 GiB (4.885 BPW)

Final estimate: PPL = 5.1257 +/- 0.03285

👈 Secret Recipe

bash

#!/usr/bin/env bash

# Repeating Layers [0-61]

custom="
# Attention
blk\..*\.attn_q.*=iq6_k
blk\..*\.attn_k.*=q8_0
blk\..*\.attn_v.*=q8_0
blk\..*\.attn_output.*=iq6_k

# Routed Experts
blk\..*\.ffn_down_exps\.weight=iq5_k
blk\..*\.ffn_(gate|up)_exps\.weight=iq4_k

# Non-Repeating Layers
token_embd\.weight=iq6_k
output\.weight=iq6_k
"

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

numactl -N 0 -m 0 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/imatrix-Qwen3-Coder-480B-A35B-Instruct-Q8_0.dat \
    /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/Qwen3-Coder-480B-A35B-Instruct-BF16-00001-of-00021.gguf \
    /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/Qwen3-Coder-480B-A35B-Instruct-IQ4_K.gguf \
    IQ4_K \
    192

`IQ4_KSS` 233.676 GiB (4.180 BPW)

Final estimate: PPL = 5.1579 +/- 0.03304

👈 Secret Recipe

bash

#!/usr/bin/env bash

# Repeating Layers [0-61]

custom="
# Attention
blk\..*\.attn_q.*=iq6_k
blk\..*\.attn_k.*=q8_0
blk\..*\.attn_v.*=q8_0
blk\..*\.attn_output.*=iq6_k

# Routed Experts
blk\.(0|1|2)\.ffn_down_exps\.weight=iq5_ks
blk\.(0|1|2)\.ffn_(gate|up)_exps\.weight=iq4_ks
blk\..*\.ffn_down_exps\.weight=iq4_ks
blk\..*\.ffn_(gate|up)_exps\.weight=iq4_kss

# Non-Repeating Layers
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

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

numactl -N 0 -m 0 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/imatrix-Qwen3-Coder-480B-A35B-Instruct-Q8_0.dat \
    /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/Qwen3-Coder-480B-A35B-Instruct-BF16-00001-of-00021.gguf \
    /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/Qwen3-Coder-480B-A35B-Instruct-IQ4_KSS.gguf \
    IQ4_KSS \
    192

`IQ3_K` 216.047 GiB (3.865 BPW)

Final estimate: PPL = 5.1808 +/- 0.03319

👈 Secret Recipe

bash

#!/usr/bin/env bash

# Repeating Layers [0-61]

custom="
# Attention
blk\..*\.attn_q.*=iq6_k
blk\..*\.attn_k.*=q8_0
blk\..*\.attn_v.*=q8_0
blk\..*\.attn_output.*=iq6_k

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

# Non-Repeating Layers
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

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

numactl -N 0 -m 0 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/imatrix-Qwen3-Coder-480B-A35B-Instruct-Q8_0.dat \
    /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/Qwen3-Coder-480B-A35B-Instruct-BF16-00001-of-00021.gguf \
    /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/Qwen3-Coder-480B-A35B-Instruct-IQ3_K.gguf \
    IQ3_K \
    192

`IQ2_KL` 169.597 GiB (3.034 BPW)

Final estimate: PPL = 5.4113 +/- 0.03516

👈 Secret Recipe

Originally this had issues with NaNs but got it working this this PR: https://github.com/ikawrakow/ik_llama.cpp/pull/735

bash

#!/usr/bin/env bash

custom="
# Attention
blk\..*\.attn_q.*=iq6_k
blk\..*\.attn_k.*=q8_0
blk\..*\.attn_v.*=q8_0
blk\..*\.attn_output.*=iq6_k

# Routed Experts
blk\..*\.ffn_down_exps\.weight=iq3_k
blk\..*\.ffn_(gate|up)_exps\.weight=iq2_kl

# Non-Repeating Layers
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

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

numactl -N 0 -m 0 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/imatrix-Qwen3-Coder-480B-A35B-Instruct-Q8_0.dat \
    /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/Qwen3-Coder-480B-A35B-Instruct-BF16-00001-of-00021.gguf \
    /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/Qwen3-Coder-480B-A35B-Instruct-IQ2_KL.gguf \
    IQ2_KL \
    192

`IQ2_K` 144.640 GiB (2.588 BPW)

Final estimate: PPL = 5.6578 +/- 0.03697

This is a lite-IQ2_K given it uses iq2_kl for ffn_down_exps instead of usual IQ3_K. This size and PPL is almost identical to an IQ2_KS.

👈 Secret Recipe

bash

#!/usr/bin/env bash

# Repeating Layers [0-61]

custom="
# Attention
blk\..*\.attn_q.*=iq6_k
blk\..*\.attn_k.*=q8_0
blk\..*\.attn_v.*=q8_0
blk\..*\.attn_output.*=iq6_k

# Routed Experts
blk\..*\.ffn_down_exps\.weight=iq2_kl
blk\..*\.ffn_(gate|up)_exps\.weight=iq2_k

# Non-Repeating Layers
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

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

numactl -N 0 -m 0 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/imatrix-Qwen3-Coder-480B-A35B-Instruct-Q8_0.dat \
    /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/Qwen3-Coder-480B-A35B-Instruct-BF16-00001-of-00021.gguf \
    /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/Qwen3-Coder-480B-A35B-Instruct-IQ2_K.gguf \
    IQ2_K \
    192

`IQ2_KS` 144.126 GiB (2.578 BPW)

Final estimate: PPL = 5.6658 +/- 0.03716

👈 Secret Recipe

bash

#!/usr/bin/env bash

# Repeating Layers [0-61]

custom="
# Attention
blk\..*\.attn_q.*=iq4_ks
blk\..*\.attn_k.*=iq5_ks
blk\..*\.attn_v.*=iq5_ks
blk\..*\.attn_output.*=iq4_ks

# Routed Experts
blk\..*\.ffn_down_exps\.weight=iq3_ks
blk\..*\.ffn_(gate|up)_exps\.weight=iq2_ks

# Non-Repeating Layers
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

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

numactl -N 0 -m 0 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/imatrix-Qwen3-Coder-480B-A35B-Instruct-Q8_0.dat \
    /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/Qwen3-Coder-480B-A35B-Instruct-BF16-00001-of-00021.gguf \
    /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/Qwen3-Coder-480B-A35B-Instruct-IQ2_KS.gguf \
    IQ2_KS \
    192

`IQ1_KT` 108.702 GiB (1.945 BPW)

Final estimate: PPL = 6.3370 +/- 0.04289

This is mostly for full GPU offload as the KT quants tend to be CPU-bound for TG while calculating Trellis. However, PP is very good.

👈 Secret Recipe

bash

blk\..*\.attn_q.*=iq4_kt
blk\..*\.attn_k.*=iq4_kt
blk\..*\.attn_v.*=iq4_kt
blk\..*\.attn_output.*=iq4_kt

# Routed Experts
blk\..*\.ffn_down_exps\.weight=iq2_kt
blk\..*\.ffn_(gate|up)_exps\.weight=iq1_kt

# Non-Repeating Layers
token_embd\.weight=iq4_kt
output\.weight=iq6_k
"

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

numactl -N 0 -m 0 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/imatrix-Qwen3-Coder-480B-A35B-Instruct-Q8_0.dat \
    /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/Qwen3-Coder-480B-A35B-Instruct-BF16-00001-of-00021.gguf \
    /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/Qwen3-Coder-480B-A35B-Instruct-IQ1_KT.gguf \
    IQ1_KT \
    192

Quick Start

This example is for a single CUDA GPU hybrid infrencing with CPU/RAM. Check ik_llama.cpp discussions or my other quants for more examples for multi-GPU etc.

bash

./build/bin/llama-server \
  --model /models/IQ2_KS/Qwen3-Coder-480B-A35B-Instruct-IQ2_KS.gguf \
  --alias ubergarm/Qwen3-Coder-480B-A35B-Instruct \
  -fa -fmoe \
  -ctk q8_0 -ctv q8_0 \
  -c 32768 \
  -ngl 99 \
  -ot "blk\.[0-9]\.ffn.*=CUDA0" \
  -ot "blk.*\.ffn.*=CPU \
  --threads 16 \
  -ub 4096 -b 4096 \
  --host 127.0.0.1 \
  --port 8080

ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF

ik_llama.cpp imatrix Quantizations of Qwen/Qwen3-Coder-480B-A35B-Instruct

Big Thanks

Quant Collection

IQ5_K 329.804 GiB (5.900 BPW)

IQ4_K 273.041 GiB (4.885 BPW)

IQ4_KSS 233.676 GiB (4.180 BPW)

IQ3_K 216.047 GiB (3.865 BPW)

IQ2_KL 169.597 GiB (3.034 BPW)

IQ2_K 144.640 GiB (2.588 BPW)

IQ2_KS 144.126 GiB (2.578 BPW)

IQ1_KT 108.702 GiB (1.945 BPW)

Quick Start

References

ubergarm/
Qwen3-Coder-480B-A35B-Instruct-GGUF

`ik_llama.cpp` imatrix Quantizations of Qwen/Qwen3-Coder-480B-A35B-Instruct

`IQ5_K` 329.804 GiB (5.900 BPW)

`IQ4_K` 273.041 GiB (4.885 BPW)

`IQ4_KSS` 233.676 GiB (4.180 BPW)

`IQ3_K` 216.047 GiB (3.865 BPW)

`IQ2_KL` 169.597 GiB (3.034 BPW)

`IQ2_K` 144.640 GiB (2.588 BPW)

`IQ2_KS` 144.126 GiB (2.578 BPW)

`IQ1_KT` 108.702 GiB (1.945 BPW)