---
quantized_by: ubergarm
pipeline_tag: text-generation
base_model: moonshotai/Kimi-K2-Instruct
license: other
license_name: modified-mit
license_link: https://huggingface.co/moonshotai/Kimi-K2-Instruct/raw/main/LICENSE
base_model_relation: quantized
tags:
- mla
- imatrix
- conversational
- ik_llama.cpp
---

## `ik_llama.cpp` imatrix Quantizations of moonshotai/Kimi-K2-Instruct
This quant collection **REQUIRES** [ik_llama.cpp](https://github.com/ikawrakow/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](https://github.com/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](https://forum.level1techs.com/t/deepseek-deep-dive-r1-at-home/225826), [YouTube Channel](https://www.youtube.com/@Level1Techs)!  **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](https://huggingface.co/BeaverAI) and on [r/LocalLLaMA](https://www.reddit.com/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](https://huggingface.co/ubergarm/Kimi-K2-Instruct-GGUF/discussions/4), 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](images/perplexity.png "Chart showing Perplexity improving as BPW increases.")

### * v0.2 `IQ4_KS` 554.421 GiB (4.638 BPW)
Final estimate: PPL = 2.9584 +/- 0.01473

<details>

<summary>👈 Secret Recipe</summary>

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

```bash
#!/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
```

</details>

### * v0.2 `IQ3_KS` 430.908 GiB (3.604 BPW)
Final estimate: PPL = 3.0226 +/- 0.01518

<details>

<summary>👈 Secret Recipe</summary>

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

```bash
#!/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
```

</details>


### * v0.2 `IQ2_KL` 349.389 GiB (2.923 BPW)
Final estimate: PPL = 3.1813 +/- 0.01619

<details>

<summary>👈 Secret Recipe</summary>

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

```bash
#!/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
```

</details>

### * v0.2 `smol-IQ2_KL` 329.702 GiB (2.758 BPW)
Final estimate: PPL = 3.4086 +/- 0.01773

<details>

<summary>👈 Secret Recipe</summary>

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

```bash
#!/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
```

</details>

### * v0.2 `IQ2_KS` 290.327 GiB (2.429 BPW)
Final estimate: PPL = 3.6827 +/- 0.01957

<details>

<summary>👈 Secret Recipe</summary>

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

```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_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
```

</details>

### * v0.2 `IQ1_KT` 234.141 GiB (1.959 BPW)
Final estimate: PPL = 3.9734 +/- 0.02152

<details>

<summary>👈 Secret Recipe</summary>

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

```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_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
```

</details>



### * v0.2 `smol-IQ1_KT` 219.375 GiB (1.835 BPW)
Final estimate: PPL = 4.2187 +/- 0.02325

<details>

<summary>👈 Secret Recipe</summary>

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

```bash
#!/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
```

</details>


## Example Commands
### Hybrid (multiple) CUDA + CPU
```bash
# 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)
```bash
# 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
```

## References
* [ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp)
* [Getting Started Guide (already out of date lol)](https://github.com/ikawrakow/ik_llama.cpp/discussions/258)
* [mainline llama.cpp PR](https://github.com/ggml-org/llama.cpp/pull/14654)
* [gabriellarsion PR author test repo discussion](https://huggingface.co/gabriellarson/Kimi-K2-Instruct-GGUF/discussions/1)