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---
quantized_by: ubergarm
pipeline_tag: text-generation
base_model: moonshotai/Kimi-K2-Instruct-0905
license: other
license_name: modified-mit
license_link: https://huggingface.co/moonshotai/Kimi-K2-Instruct-0905/blob/main/LICENSE
base_model_relation: quantized
tags:
- mla
- imatrix
- conversational
- ik_llama.cpp
---
## `ik_llama.cpp` imatrix Quantizations of moonshotai/Kimi-K2-Instruct-0905
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. For pre-built Windows binaries of ik_llama.cpp check out [Thireus' fork here](https://github.com/Thireus/ik_llama.cpp/releases).
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!
## Notes
* The current imatrix dat file seems to be missing entries for just the single dense layer and shared expert so all my recipes are using `q8_0` for those.
* For notes on tool calling api endpoints checkout details from this PR: https://github.com/ikawrakow/ik_llama.cpp/pull/723
* `smol` here simply means the routed experts recipe uses the same quantization for down as well as (gate|up) tensors.
## Quant Collection
Compare with baseline perplexity of full size `Q8_0` 1016.117 GiB (8.504 BPW)
Final estimate: PPL = 2.4443 +/- 0.01175
### `smol-IQ5_KS` 632.664 GiB (5.295 BPW)
Final estimate: PPL = 2.4526 +/- 0.01182
<details>
<summary>👈 Secret Recipe</summary>
```bash
#!/usr/bin/env bash
custom="
## Attention [0-60] (GPU)
blk\..*\.attn_k_b\.weight=q8_0
blk\..*\.attn_v_b\.weight=q8_0
# Balance of attn tensors
blk\..*\.attn_kv_a_mqa\.weight=q8_0
blk\..*\.attn_q_a\.weight=q8_0
blk\..*\.attn_q_b\.weight=q8_0
blk\..*\.attn_output\.weight=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=iq5_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:,$::;s:^,::'
)
numactl -N 0 -m 0 \
./build/bin/llama-quantize \
--custom-q "$custom" \
--imatrix /mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/imatrix-Kimi-K2-Instruct-0905-Q8_0.dat \
/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-384x14B-Instruct-safetensors-0905-BF16-00001-of-00046.gguf \
/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-Instruct-0905-smol-IQ5_KS.gguf \
IQ5_KS \
192
```
</details>
### `smol-IQ4_KSS` 485.008 GiB (4.059 BPW)
Final estimate: PPL = 2.5185 +/- 0.01221
<details>
<summary>👈 Secret Recipe</summary>
```bash
#!/usr/bin/env bash
custom="
## Attention [0-60] (GPU)
blk\..*\.attn_k_b\.weight=q8_0
blk\..*\.attn_v_b\.weight=q8_0
# Balance of attn tensors
blk\..*\.attn_kv_a_mqa\.weight=q8_0
blk\..*\.attn_q_a\.weight=q8_0
blk\..*\.attn_q_b\.weight=q8_0
blk\..*\.attn_output\.weight=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_kss
blk\..*\.ffn_(gate|up)_exps\.weight=iq4_kss
## 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:,$::;s:^,::'
)
numactl -N 0 -m 0 \
./build/bin/llama-quantize \
--custom-q "$custom" \
--imatrix /mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/imatrix-Kimi-K2-Instruct-0905-Q8_0.dat \
/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-384x14B-Instruct-safetensors-0905-BF16-00001-of-00046.gguf \
/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-Instruct-0905-smol-IQ4_KSS.gguf \
IQ4_KSS \
192
```
</details>
### `IQ4_KS` 553.624 GiB (4.633 BPW)
Final estimate: PPL = 2.4641 +/- 0.01190
<details>
<summary>👈 Secret Recipe</summary>
```bash
#!/usr/bin/env bash
custom="
## Attention [0-60] (GPU)
blk\..*\.attn_k_b\.weight=q8_0
blk\..*\.attn_v_b\.weight=q8_0
# Balance of attn tensors
blk\..*\.attn_kv_a_mqa\.weight=q8_0
blk\..*\.attn_q_a\.weight=q8_0
blk\..*\.attn_q_b\.weight=q8_0
blk\..*\.attn_output\.weight=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=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/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/imatrix-Kimi-K2-Instruct-0905-Q8_0.dat \
/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-384x14B-Instruct-safetensors-0905-BF16-00001-of-00046.gguf \
/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-Instruct-0905-IQ4_KS.gguf \
IQ4_KS \
192
```
</details>
### `IQ3_KS` 420.558 GiB (3.520 BPW)
Final estimate: PPL = 2.5640 +/- 0.01262
<details>
<summary>👈 Secret Recipe</summary>
```bash
#!/usr/bin/env bash
custom="
## Attention [0-60] (GPU)
blk\..*\.attn_k_b\.weight=q8_0
blk\..*\.attn_v_b\.weight=q8_0
# Balance of attn tensors
blk\..*\.attn_kv_a_mqa\.weight=q8_0
blk\..*\.attn_q_a\.weight=q8_0
blk\..*\.attn_q_b\.weight=q8_0
blk\..*\.attn_output\.weight=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_kss
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:,$::;s:^,::'
)
numactl -N 0 -m 0 \
./build/bin/llama-quantize \
--custom-q "$custom" \
--imatrix /mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/imatrix-Kimi-K2-Instruct-0905-Q8_0.dat \
/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-384x14B-Instruct-safetensors-0905-BF16-00001-of-00046.gguf \
/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-Instruct-0905-IQ3_KS.gguf \
IQ3_KS \
192
```
</details>
### `smol-IQ3_KS` 388.258 GiB (3.249 BPW)
Final estimate: PPL = 2.5902 +/- 0.01284
<details>
<summary>👈 Secret Recipe</summary>
```bash
#!/usr/bin/env bash
custom="
## Attention [0-60] (GPU)
blk\..*\.attn_k_b\.weight=q8_0
blk\..*\.attn_v_b\.weight=q8_0
# Balance of attn tensors
blk\..*\.attn_kv_a_mqa\.weight=q8_0
blk\..*\.attn_q_a\.weight=q8_0
blk\..*\.attn_q_b\.weight=q8_0
blk\..*\.attn_output\.weight=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=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:,$::;s:^,::'
)
numactl -N 0 -m 0 \
./build/bin/llama-quantize \
--custom-q "$custom" \
--imatrix /mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/imatrix-Kimi-K2-Instruct-0905-Q8_0.dat \
/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-384x14B-Instruct-safetensors-0905-BF16-00001-of-00046.gguf \
/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-Instruct-0905-smol-IQ3_KS.gguf \
IQ3_KS \
192
```
</details>
### `IQ2_KL` 358.419 GiB (3.000 BPW)
Final estimate: PPL = 2.7993 +/- 0.01416
<details>
<summary>👈 Secret Recipe</summary>
```bash
#!/usr/bin/env bash
custom="
## Attention [0-60] (GPU)
blk\..*\.attn_k_b\.weight=q8_0
blk\..*\.attn_v_b\.weight=q8_0
# Balance of attn tensors
blk\..*\.attn_kv_a_mqa\.weight=q8_0
blk\..*\.attn_q_a\.weight=q8_0
blk\..*\.attn_q_b\.weight=q8_0
blk\..*\.attn_output\.weight=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_k
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:,$::;s:^,::'
)
numactl -N 0 -m 0 \
./build/bin/llama-quantize \
--custom-q "$custom" \
--imatrix /mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/imatrix-Kimi-K2-Instruct-0905-Q8_0.dat \
/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-384x14B-Instruct-safetensors-0905-BF16-00001-of-00046.gguf \
/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-Instruct-0905-IQ2_KL.gguf \
IQ2_KL \
192
```
</details>
### `smol-IQ2_KL` 329.195 GiB (2.755 BPW)
Final estimate: PPL = 2.9294 +/- 0.01499
<details>
<summary>👈 Secret Recipe</summary>
```bash
#!/usr/bin/env bash
custom="
## Attention [0-60] (GPU)
blk\..*\.attn_k_b\.weight=q8_0
blk\..*\.attn_v_b\.weight=q8_0
# Balance of attn tensors
blk\..*\.attn_kv_a_mqa\.weight=q8_0
blk\..*\.attn_q_a\.weight=q8_0
blk\..*\.attn_q_b\.weight=q8_0
blk\..*\.attn_output\.weight=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/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/imatrix-Kimi-K2-Instruct-0905-Q8_0.dat \
/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-384x14B-Instruct-safetensors-0905-BF16-00001-of-00046.gguf \
/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-Instruct-0905-smol-IQ2_KL.gguf \
IQ2_KL \
192
```
</details>
### `IQ2_KS` 289.820 GiB (2.425 BPW)
Final estimate: PPL = 3.2478 +/- 0.01721
<details>
<summary>👈 Secret Recipe</summary>
```bash
#!/usr/bin/env bash
custom="
## Attention [0-60] (GPU)
blk\..*\.attn_k_b\.weight=q8_0
blk\..*\.attn_v_b\.weight=q8_0
# Balance of attn tensors
blk\..*\.attn_kv_a_mqa\.weight=q8_0
blk\..*\.attn_q_a\.weight=q8_0
blk\..*\.attn_q_b\.weight=q8_0
blk\..*\.attn_output\.weight=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/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/imatrix-Kimi-K2-Instruct-0905-Q8_0.dat \
/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-384x14B-Instruct-safetensors-0905-BF16-00001-of-00046.gguf \
/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-Instruct-0905-IQ2_KS.gguf \
IQ2_KS \
192
```
</details>
### `smol-IQ2_KS` 270.133 GiB (2.261 BPW)
Final estimate: PPL = 3.4977 +/- 0.01924
<details>
<summary>👈 Secret Recipe</summary>
```bash
#!/usr/bin/env bash
custom="
## Attention [0-60] (GPU)
blk\..*\.attn_k_b\.weight=q8_0
blk\..*\.attn_v_b\.weight=q8_0
# Balance of attn tensors
blk\..*\.attn_kv_a_mqa\.weight=q8_0
blk\..*\.attn_q_a\.weight=q8_0
blk\..*\.attn_q_b\.weight=q8_0
blk\..*\.attn_output\.weight=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_ks
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:,$::;s:^,::'
)
numactl -N 0 -m 0 \
./build/bin/llama-quantize \
--custom-q "$custom" \
--imatrix /mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/imatrix-Kimi-K2-Instruct-0905-Q8_0.dat \
/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-384x14B-Instruct-safetensors-0905-BF16-00001-of-00046.gguf \
/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-Instruct-0905-smol-IQ2_KS.gguf \
IQ2_KS \
192
```
</details>
### `smol-IQ1_KT` 218.936 GiB (1.832 BPW)
Final estimate: PPL = 4.2224 +/- 0.02443
<details>
<summary>👈 Secret Recipe</summary>
```bash
#!/usr/bin/env bash
custom="
## Attention [0-60] (GPU)
blk\..*\.attn_k_b\.weight=q8_0
blk\..*\.attn_v_b\.weight=q8_0
# Balance of attn tensors
blk\..*\.attn_kv_a_mqa\.weight=q8_0
blk\..*\.attn_q_a\.weight=q8_0
blk\..*\.attn_q_b\.weight=q8_0
blk\..*\.attn_output\.weight=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_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/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/imatrix-Kimi-K2-Instruct-0905-Q8_0.dat \
/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-384x14B-Instruct-safetensors-0905-BF16-00001-of-00046.gguf \
/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-Instruct-0905-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-0905 \
--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-0905 \
--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)
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