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Kimi-K2-Instruct-GGUF

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ik_llama.cpp
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10

ik_llama.cpp imatrix Quantizations of moonshotai/Kimi-K2-Instruct

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.

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!

UPDATED RECIPES

Updated new better lower perplexity recipes and worlds smallest Kimi-K2-Instruct-smol-IQ1_KT at 219.375 GIB (1.835) BPW. Please ask any questions in this discussion here, thanks!

Old versions still available as described in the dicsussion at tag/revision v0.1.

Quant Collection

Compare with Perplexity of full size Q8_0 1016.623 GiB (8.504 BPW):

Final estimate: PPL = 2.9507 +/- 0.01468

Perplexity Chart

* v0.2 IQ4_KS 554.421 GiB (4.638 BPW)

Final estimate: PPL = 2.9584 +/- 0.01473

👈 Secret Recipe

Special mix of IQ4_KS ffn_(gate|up)_exps and IQ5_KS ffn_down_exps routed experts.

#!/usr/bin/env bash

custom="
## Attention [0-60] (GPU)
# Only ik's fork uses this, keep it q8_0 as its only for PP with -mla 3
blk\..*\.attn_kv_b\.weight=q8_0

# ideally k_b and v_b are smaller than q8_0 as they are is used for TG with -mla 3 (and ik's imatrix supports it)
# blk.*.attn_k_b.weight is not divisible by 256 so only supports qN_0 or iq4_nl
blk\..*\.attn_k_b\.weight=q8_0

# Balance of attn tensors
blk\..*\.attn_.*=q8_0

## First Single Dense Layer [0] (GPU)
blk\..*\.ffn_down\.weight=q8_0
blk\..*\.ffn_(gate|up)\.weight=q8_0

## Shared Expert [1-60] (GPU)
blk\..*\.ffn_down_shexp\.weight=q8_0
blk\..*\.ffn_(gate|up)_shexp\.weight=q8_0

## Routed Experts [1-60] (CPU)
blk\..*\.ffn_down_exps\.weight=iq5_ks
blk\..*\.ffn_(gate|up)_exps\.weight=iq4_ks

## Token embedding and output tensors (GPU)
token_embd\.weight=iq6_k
output\.weight=iq6_k
"

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

numactl -N 1 -m 1 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Kimi-K2-Instruct-GGUF/imatrix-Kimi-K2-Instruct-Q8_0.dat \
    /mnt/raid/models/ubergarm/Kimi-K2-Instruct-GGUF/Kimi-K2-384x15B-Instruct-safetensors-BF16-00001-of-00045.gguf \
    /mnt/raid/models/ubergarm/Kimi-K2-Instruct-GGUF/Kimi-K2-Instruct-IQ4_KS.gguf \
    IQ4_KS \
    192

* v0.2 IQ3_KS 430.908 GiB (3.604 BPW)

Final estimate: PPL = 3.0226 +/- 0.01518

👈 Secret Recipe

Special mix of IQ3_KS ffn_(gate|up)_exps and IQ4_KS ffn_down_exps routed experts.

#!/usr/bin/env bash

custom="
## Attention [0-60] (GPU)
# Only ik's fork uses this, keep it q8_0 as its only for PP with -mla 3
blk\..*\.attn_kv_b\.weight=q8_0

# ideally k_b and v_b are smaller than q8_0 as they are is used for TG with -mla 3 (and ik's imatrix supports it)
# blk.*.attn_k_b.weight is not divisible by 256 so only supports qN_0 or iq4_nl
blk\..*\.attn_k_b\.weight=q8_0

# Balance of attn tensors
blk\..*\.attn_.*=q8_0

## First Single Dense Layer [0] (GPU)
blk\..*\.ffn_down\.weight=q8_0
blk\..*\.ffn_(gate|up)\.weight=q8_0

## Shared Expert [1-60] (GPU)
blk\..*\.ffn_down_shexp\.weight=q8_0
blk\..*\.ffn_(gate|up)_shexp\.weight=q8_0

## Routed Experts [1-60] (CPU)
blk\..*\.ffn_down_exps\.weight=iq4_ks
blk\..*\.ffn_(gate|up)_exps\.weight=iq3_ks

## Token embedding and output tensors (GPU)
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

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

numactl -N 1 -m 1 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Kimi-K2-Instruct-GGUF/imatrix-Kimi-K2-Instruct-Q8_0.dat \
    /mnt/raid/models/ubergarm/Kimi-K2-Instruct-GGUF/Kimi-K2-384x15B-Instruct-safetensors-BF16-00001-of-00045.gguf \
    /mnt/raid/models/ubergarm/Kimi-K2-Instruct-GGUF/Kimi-K2-Instruct-IQ3_KS.gguf \
    IQ3_KS \
    192

* v0.2 IQ2_KL 349.389 GiB (2.923 BPW)

Final estimate: PPL = 3.1813 +/- 0.01619

👈 Secret Recipe

Special mix with brand new SOTA IQ2_KL ffn_(gate|up)_exps and IQ3_KS ffn_down_exps routed experts.

#!/usr/bin/env bash

custom="
## Attention [0-60] (GPU)
# Only ik's fork uses this, keep it q8_0 as its only for PP with -mla 3
blk\..*\.attn_kv_b\.weight=q8_0

# ideally k_b and v_b are smaller than q8_0 as they are is used for TG with -mla 3 (and ik's imatrix supports it)
# blk.*.attn_k_b.weight is not divisible by 256 so only supports qN_0 or iq4_nl
blk\..*\.attn_k_b\.weight=q8_0

# Balance of attn tensors
blk\..*\.attn_.*=q8_0

## First Single Dense Layer [0] (GPU)
blk\..*\.ffn_down\.weight=q8_0
blk\..*\.ffn_(gate|up)\.weight=q8_0

## Shared Expert (1-60) (GPU)
blk\..*\.ffn_down_shexp\.weight=q8_0
blk\..*\.ffn_(gate|up)_shexp\.weight=q8_0

## Routed Experts (1-60) (CPU)
blk\..*\.ffn_down_exps\.weight=iq3_ks
blk\..*\.ffn_(gate|up)_exps\.weight=iq2_kl

## Token embedding and output tensors (GPU)
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

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

numactl -N 1 -m 1 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Kimi-K2-Instruct-GGUF/imatrix-Kimi-K2-Instruct-Q8_0.dat \
    /mnt/raid/models/ubergarm/Kimi-K2-Instruct-GGUF/Kimi-K2-384x15B-Instruct-safetensors-BF16-00001-of-00045.gguf \
    /mnt/raid/models/ubergarm/Kimi-K2-Instruct-GGUF/Kimi-K2-Instruct-IQ2_KL.gguf \
    IQ2_KL \
    192

* v0.2 smol-IQ2_KL 329.702 GiB (2.758 BPW)

Final estimate: PPL = 3.4086 +/- 0.01773

👈 Secret Recipe

Special mix of IQ2_KL ffn_(gate|up)_exps and also IQ2_KL ffn_down_exps routed experts.

#!/usr/bin/env bash

custom="
## Attention [0-60] (GPU)
# Only ik's fork uses this, keep it q8_0 as its only for PP with -mla 3
blk\..*\.attn_kv_b\.weight=q8_0

# ideally k_b and v_b are smaller than q8_0 as they are is used for TG with -mla 3 (and ik's imatrix supports it)
# blk.*.attn_k_b.weight is not divisible by 256 so only supports qN_0 or iq4_nl
blk\..*\.attn_k_b\.weight=q8_0

# Balance of attn tensors
blk\..*\.attn_.*=q8_0

## First Single Dense Layer [0] (GPU)
blk\..*\.ffn_down\.weight=q8_0
blk\..*\.ffn_(gate|up)\.weight=q8_0

## Shared Expert (1-60) (GPU)
blk\..*\.ffn_down_shexp\.weight=q8_0
blk\..*\.ffn_(gate|up)_shexp\.weight=q8_0

## Routed Experts (1-60) (CPU)
blk\..*\.ffn_down_exps\.weight=iq2_kl
blk\..*\.ffn_(gate|up)_exps\.weight=iq2_kl

## Token embedding and output tensors (GPU)
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

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

numactl -N 1 -m 1 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Kimi-K2-Instruct-GGUF/imatrix-Kimi-K2-Instruct-Q8_0.dat \
    /mnt/raid/models/ubergarm/Kimi-K2-Instruct-GGUF/Kimi-K2-384x15B-Instruct-safetensors-BF16-00001-of-00045.gguf \
    /mnt/raid/models/ubergarm/Kimi-K2-Instruct-GGUF/Kimi-K2-Instruct-bigattnshexpdense-smol-IQ2_KL.gguf \
    IQ2_KL \
    192

* v0.2 IQ2_KS 290.327 GiB (2.429 BPW)

Final estimate: PPL = 3.6827 +/- 0.01957

👈 Secret Recipe

Special mix with IQ2_KS ffn_(gate|up)_exps and band new SOTA IQ2_KL ffn_down_exps routed experts.

custom="
## Attention [0-60] (GPU)
# Only ik's fork uses this, keep it q8_0 as its only for PP with -mla 3
blk\..*\.attn_kv_b\.weight=q8_0

# ideally k_b and v_b are smaller than q8_0 as they are is used for TG with -mla 3 (and ik's imatrix supports it)
# blk.*.attn_k_b.weight is not divisible by 256 so only supports qN_0 or iq4_nl
blk\..*\.attn_k_b\.weight=q8_0

# Balance of attn tensors
blk\..*\.attn_.*=q8_0

## First Single Dense Layer [0] (GPU)
blk\..*\.ffn_down\.weight=q8_0
blk\..*\.ffn_(gate|up)\.weight=q8_0

## Shared Expert [1-60] (GPU)
blk\..*\.ffn_down_shexp\.weight=q8_0
blk\..*\.ffn_(gate|up)_shexp\.weight=q8_0

## Routed Experts [1-60] (CPU)
blk\..*\.ffn_down_exps\.weight=iq2_kl
blk\..*\.ffn_(gate|up)_exps\.weight=iq2_ks

## Token embedding and output tensors (GPU)
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

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

numactl -N 1 -m 1 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Kimi-K2-Instruct-GGUF/imatrix-Kimi-K2-Instruct-Q8_0.dat \
    /mnt/raid/models/ubergarm/Kimi-K2-Instruct-GGUF/Kimi-K2-384x15B-Instruct-safetensors-BF16-00001-of-00045.gguf \
    /mnt/raid/models/ubergarm/Kimi-K2-Instruct-GGUF/Kimi-K2-Instruct-IQ2_KS.gguf \
    IQ2_KS \
    192

* v0.2 IQ1_KT 234.141 GiB (1.959 BPW)

Final estimate: PPL = 3.9734 +/- 0.02152

👈 Secret Recipe

Special mix of IQ1_KT ffn_(gate|up)_exps and IQ2_KT ffn_down_exps routed experts.

custom="
## Attention [0-60] (GPU)
# Only ik's fork uses this, keep it q8_0 as its only for PP with -mla 3
blk\..*\.attn_kv_b\.weight=q8_0

# ideally k_b and v_b are smaller than q8_0 as they are is used for TG with -mla 3 (and ik's imatrix supports it)
# blk.*.attn_k_b.weight is not divisible by 256 so only supports qN_0 or iq4_nl
blk\..*\.attn_k_b\.weight=q8_0

# Balance of attn tensors
blk\..*\.attn_.*=q8_0

## First Single Dense Layer [0] (GPU)
blk\..*\.ffn_down\.weight=q8_0
blk\..*\.ffn_(gate|up)\.weight=q8_0

## Shared Expert [1-60] (GPU)
blk\..*\.ffn_down_shexp\.weight=q8_0
blk\..*\.ffn_(gate|up)_shexp\.weight=q8_0

## Routed Experts [1-60] (CPU)
blk\..*\.ffn_down_exps\.weight=iq2_kt
blk\..*\.ffn_(gate|up)_exps\.weight=iq1_kt

## Token embedding and output tensors (GPU)
token_embd\.weight=iq4_kt
output\.weight=iq6_k
"

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

numactl -N 1 -m 1 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Kimi-K2-Instruct-GGUF/imatrix-Kimi-K2-Instruct-Q8_0.dat \
    /mnt/raid/models/ubergarm/Kimi-K2-Instruct-GGUF/Kimi-K2-384x15B-Instruct-safetensors-BF16-00001-of-00045.gguf \
    /mnt/raid/models/ubergarm/Kimi-K2-Instruct-GGUF/Kimi-K2-Instruct-bigattnshexpdense-IQ1_KT.gguf \
    IQ1_KT \
    192

* v0.2 smol-IQ1_KT 219.375 GiB (1.835 BPW)

Final estimate: PPL = 4.2187 +/- 0.02325

👈 Secret Recipe

Special mix of IQ1_KT ffn_(gate|up)_exps and also IQ1_KT ffn_down_exps routed experts.

#!/usr/bin/env bash

custom="
## Attention [0-60] (GPU)
# Only ik's fork uses this, keep it q8_0 as its only for PP with -mla 3
blk\..*\.attn_kv_b\.weight=q8_0

# ideally k_b and v_b are smaller than q8_0 as they are is used for TG with -mla 3 (and ik's imatrix supports it)
# blk.*.attn_k_b.weight is not divisible by 256 so only supports qN_0 or iq4_nl
blk\..*\.attn_k_b\.weight=q8_0

# Balance of attn tensors
blk\..*\.attn_.*=q8_0

## First Single Dense Layer [0] (GPU)
blk\..*\.ffn_down\.weight=q8_0
blk\..*\.ffn_(gate|up)\.weight=q8_0

## Shared Expert [1-60] (GPU)
blk\..*\.ffn_down_shexp\.weight=q8_0
blk\..*\.ffn_(gate|up)_shexp\.weight=q8_0

## Routed Experts [1-60] (CPU)
blk\..*\.ffn_down_exps\.weight=iq1_kt
blk\..*\.ffn_(gate|up)_exps\.weight=iq1_kt

## Token embedding and output tensors (GPU)
token_embd\.weight=iq4_kt
output\.weight=iq6_k
"

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

numactl -N 1 -m 1 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Kimi-K2-Instruct-GGUF/imatrix-Kimi-K2-Instruct-Q8_0.dat \
    /mnt/raid/models/ubergarm/Kimi-K2-Instruct-GGUF/Kimi-K2-384x15B-Instruct-safetensors-BF16-00001-of-00045.gguf \
    /mnt/raid/models/ubergarm/Kimi-K2-Instruct-GGUF/Kimi-K2-Instruct-bigattnshexpdense-smol-IQ1_KT.gguf \
    IQ1_KT \
    192

Example Commands

Hybrid (multiple) CUDA + CPU 

# Two CUDA devices with enough VRAM to offload more layers
# Keep in mind Kimi-K2 starts at 1 unlike DeepSeek at 3 (first dense layers)
./build/bin/llama-server \
    --model "$model"\
    --alias ubergarm/Kimi-K2-Instruct \
    --ctx-size 32768 \
    -ctk q8_0 \
    -fa -fmoe \
    -mla 3 \
    -ngl 99 \
    -ot "blk\.(1|2|3)\.ffn_.*=CUDA0" \
    -ot "blk\.(4|5|6)\.ffn_.*=CUDA1" \
    -ot exps=CPU \
    --parallel 1 \
    --threads 48 \
    --threads-batch 64 \
    --host 127.0.0.1 \
    --port 8080

CPU-Only (no GPU) 

# compile
cmake -B build -DGGML_CUDA=0 -DGGML_BLAS=0 -DGGML_VULKAN=0
cmake --build build --config Release -j $(nproc)

# run server
# single CPU of a dual socket rig configured one NUMA per socket
numactl -N 0 -m 0 \
./build/bin/llama-server \
    --model "$model"\
    --alias ubergarm/Kimi-K2-Instruct \
    --ctx-size 98304 \
    -ctk q8_0 \
    -fa -fmoe \
    -mla 3 \
    --parallel 1 \
    --threads 128 \
    --threads-batch 192 \
    --numa numactl \
    --host 127.0.0.1 \
    --port 8080

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