---
quantized_by: ubergarm
pipeline_tag: text-generation
base_model: tngtech/DeepSeek-TNG-R1T2-Chimera
license: mit
base_model_relation: quantized
tags:
- mla
- imatrix
- conversational
- ik_llama.cpp
---

## `ik_llama.cpp` imatrix Quantizations of DeepSeek-TNG-R1T2-Chimera
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://discord.com/channels/1238219753324281886/1238239819017097246/1238676202357784650) 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!

## Quants
For some larger non-imatrix ik quant options check out [Kebob/DeepSeek-TNG-R1T2-Chimera-IK_GGUF](https://huggingface.co/Kebob/DeepSeek-TNG-R1T2-Chimera-IK_GGUF)

#### * `IQ3_KS` 281.463 GiB (3.598 BPW)
Special mix with all new `IQ3_KS` `ffn_(gate|up)_exps` and `IQ4_KS` `ffn_down_exps` routed experts. Mostly `iq5_ks/iq4_ks` for attn and shared expert. `iq5_k` `token_embd` and `iq6_k` `output` "head".

Final estimate: PPL = 3.3167 +/- 0.01789

<details>

<summary>👈 Secret Recipe</summary>

```bash
#!/usr/bin/env bash

custom="
# First 3 dense layers (0-3) (GPU)
# Except blk.*.attn_k_b.weight is not divisible by 256 so only supports qN_0
blk\.[0-2]\.attn_k_b.*=q5_0
blk\.[0-2]\.attn_.*=iq5_ks
blk\.[0-2]\.ffn_down.*=iq5_ks
blk\.[0-2]\.ffn_(gate|up).*=iq4_ks
blk\.[0-2]\..*=iq5_ks

# All attention, norm weights, and bias tensors for MoE layers (3-60) (GPU)
# Except blk.*.attn_k_b.weight is not divisible by 256 so only supports qN_0
blk\.[3-9]\.attn_k_b.*=q5_0
blk\.[1-5][0-9]\.attn_k_b.*=q5_0
blk\.60\.attn_k_b.*=q5_0

blk\.[3-9]\.attn_.*=iq5_ks
blk\.[1-5][0-9]\.attn_.*=iq5_ks
blk\.60\.attn_.*=iq5_ks

# Shared Expert (3-60) (GPU)
blk\.[3-9]\.ffn_down_shexp\.weight=iq5_ks
blk\.[1-5][0-9]\.ffn_down_shexp\.weight=iq5_ks
blk\.60\.ffn_down_shexp\.weight=iq5_ks

blk\.[3-9]\.ffn_(gate|up)_shexp\.weight=iq4_ks
blk\.[1-5][0-9]\.ffn_(gate|up)_shexp\.weight=iq4_ks
blk\.60\.ffn_(gate|up)_shexp\.weight=iq4_ks

# Routed Experts (3-60) (CPU)
blk\.[3-9]\.ffn_down_exps\.weight=iq4_ks
blk\.[1-5][0-9]\.ffn_down_exps\.weight=iq4_ks
blk\.60\.ffn_down_exps\.weight=iq4_ks

blk\.[3-9]\.ffn_(gate|up)_exps\.weight=iq3_ks
blk\.[1-5][0-9]\.ffn_(gate|up)_exps\.weight=iq3_ks
blk\.60\.ffn_(gate|up)_exps\.weight=iq3_ks

# Token embedding and output tensors (GPU)
token_embd\.weight=iq5_k
output\.weight=iq6_k
"

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

./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/DeepSeek-TNG-R1T2-Chimera-GGUF/imatrix-DeepSeek-TNG-R1T2-Chimera-Q8_0.dat \
    /mnt/raid/models/ubergarm/DeepSeek-TNG-R1T2-Chimera-GGUF/DeepSeek-TNG-R1T2-Chimera-256x21B-BF16-00001-of-00030.gguf \
    /mnt/raid/models/ubergarm/DeepSeek-TNG-R1T2-Chimera-GGUF/DeepSeek-TNG-R1T2-Chimera-IQ3_KS.gguf \
    IQ3_KS \
    24
```

</details>

#### * `IQ2_KS` 203.553 GiB (2.602 BPW)
Special mix with `IQ2_KS` `ffn_(gate|up)_exps` and new `IQ3_KS` `ffn_down_exps` routed experts. Mostly `iq5_ks/iq4_ks` for attn and shared expert. `iq5_k` `token_embd` and `iq6_k` `output` "head".

Final estimate: PPL = 3.6254 +/- 0.02001

<details>

<summary>👈 Secret Recipe</summary>

```bash
#!/usr/bin/env bash

custom="
# First 3 dense layers (0-3) (GPU)
# Except blk.*.attn_k_b.weight is not divisible by 256 so only supports qN_0
blk\.[0-2]\.attn_k_b.*=q5_0
blk\.[0-2]\.attn_.*=iq5_ks
blk\.[0-2]\.ffn_down.*=iq5_ks
blk\.[0-2]\.ffn_(gate|up).*=iq4_ks
blk\.[0-2]\..*=iq5_ks

# All attention, norm weights, and bias tensors for MoE layers (3-60) (GPU)
# Except blk.*.attn_k_b.weight is not divisible by 256 so only supports qN_0
blk\.[3-9]\.attn_k_b.*=q5_0
blk\.[1-5][0-9]\.attn_k_b.*=q5_0
blk\.60\.attn_k_b.*=q5_0

blk\.[3-9]\.attn_.*=iq5_ks
blk\.[1-5][0-9]\.attn_.*=iq5_ks
blk\.60\.attn_.*=iq5_ks

# Shared Expert (3-60) (GPU)
blk\.[3-9]\.ffn_down_shexp\.weight=iq5_ks
blk\.[1-5][0-9]\.ffn_down_shexp\.weight=iq5_ks
blk\.60\.ffn_down_shexp\.weight=iq5_ks

blk\.[3-9]\.ffn_(gate|up)_shexp\.weight=iq4_ks
blk\.[1-5][0-9]\.ffn_(gate|up)_shexp\.weight=iq4_ks
blk\.60\.ffn_(gate|up)_shexp\.weight=iq4_ks

# Routed Experts (3-60) (CPU)
blk\.[3-9]\.ffn_down_exps\.weight=iq3_ks
blk\.[1-5][0-9]\.ffn_down_exps\.weight=iq3_ks
blk\.60\.ffn_down_exps\.weight=iq3_ks

blk\.[3-9]\.ffn_(gate|up)_exps\.weight=iq2_ks
blk\.[1-5][0-9]\.ffn_(gate|up)_exps\.weight=iq2_ks
blk\.60\.ffn_(gate|up)_exps\.weight=iq2_ks

# Token embedding and output tensors (GPU)
token_embd\.weight=iq5_k
output\.weight=iq6_k
"

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

./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/DeepSeek-TNG-R1T2-Chimera-GGUF/imatrix-DeepSeek-TNG-R1T2-Chimera-Q8_0.dat \
    /mnt/raid/models/ubergarm/DeepSeek-TNG-R1T2-Chimera-GGUF/DeepSeek-TNG-R1T2-Chimera-256x21B-BF16-00001-of-00030.gguf \
    /mnt/raid/models/ubergarm/DeepSeek-TNG-R1T2-Chimera-GGUF/DeepSeek-TNG-R1T2-Chimera-IQ2_KS.gguf \
    IQ2_KS \
    24
```

</details>

#### * `IQ2_KT` 171.146 GiB (2.188 BPW)
Designed for RTX 6000 PRO Blackwell with 192GB total VRAM full offload with (hopefully) full 160k context and sufficiently large batch sizes. These `KT` quant types are quite fast on CUDA but not as fast TG on CPU inferencing.

Special mix new trellis quants (like QTIP/EXL3 style) `IQ2_KT` `ffn_(gate|down|up)_exps` routed experts. Mostly `iq4_kt/iq3_kt` for attn and shared expert. `iq4_k` `token_embd` and `iq5_k` `output` "head".

Final estimate: PPL = 3.8887 +/- 0.02191

<details>

<summary>👈 Secret Recipe</summary>

```bash
#!/usr/bin/env bash

custom="
# First 3 dense layers (0-3) (GPU)
# Except blk.*.attn_k_b.weight is not divisible by 256 so only supports qN_0
blk\.[0-2]\.attn_k_b.*=iq4_nl
blk\.[0-2]\.attn_.*=iq4_kt
blk\.[0-2]\.ffn_down.*=iq4_kt
blk\.[0-2]\.ffn_(gate|up).*=iq3_kt
blk\.[0-2]\..*=iq4_kt

# All attention, norm weights, and bias tensors for MoE layers (3-60) (GPU)
# Except blk.*.attn_k_b.weight is not divisible by 256 so only supports qN_0
blk\.[3-9]\.attn_k_b.*=iq4_nl
blk\.[1-5][0-9]\.attn_k_b.*=iq4_nl
blk\.60\.attn_k_b.*=iq4_nl

blk\.[3-9]\.attn_.*=iq4_kt
blk\.[1-5][0-9]\.attn_.*=iq4_kt
blk\.60\.attn_.*=iq4_kt

# Shared Expert (3-60) (GPU)
blk\.[3-9]\.ffn_down_shexp\.weight=iq4_kt
blk\.[1-5][0-9]\.ffn_down_shexp\.weight=iq4_kt
blk\.60\.ffn_down_shexp\.weight=iq4_kt

blk\.[3-9]\.ffn_(gate|up)_shexp\.weight=iq3_kt
blk\.[1-5][0-9]\.ffn_(gate|up)_shexp\.weight=iq3_kt
blk\.60\.ffn_(gate|up)_shexp\.weight=iq3_kt

# Routed Experts (3-60) (CPU)
blk\.[3-9]\.ffn_down_exps\.weight=iq2_kt
blk\.[1-5][0-9]\.ffn_down_exps\.weight=iq2_kt
blk\.60\.ffn_down_exps\.weight=iq2_kt

blk\.[3-9]\.ffn_(gate|up)_exps\.weight=iq2_kt
blk\.[1-5][0-9]\.ffn_(gate|up)_exps\.weight=iq2_kt
blk\.60\.ffn_(gate|up)_exps\.weight=iq2_kt

# Token embedding and output tensors (GPU)
token_embd\.weight=iq4_kt
output\.weight=iq5_k
"

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

./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/DeepSeek-TNG-R1T2-Chimera-GGUF/imatrix-DeepSeek-TNG-R1T2-Chimera-Q8_0.dat \
    /mnt/raid/models/ubergarm/DeepSeek-TNG-R1T2-Chimera-GGUF/DeepSeek-TNG-R1T2-Chimera-256x21B-BF16-00001-of-00030.gguf \
    /mnt/raid/models/ubergarm/DeepSeek-TNG-R1T2-Chimera-GGUF/DeepSeek-TNG-R1T2-Chimera-IQ2_KT.gguf \
    IQ2_KT \
    24
```

</details>


#### * `IQ2_XXS` 169.590 GiB (2.168 BPW)
Not recommended, but should be faster and better quality than the IQ1_S and okay with full offload on multi-GPU. It should be okay for hybrid CPU+GPU inference as well if this size is good for your rig. Probably want to choose the IQ2_KT for full GPU offload.

Special mix `IQ2_XXS` `ffn_(gate|up)_exps` and `IQ2_KS` `ffn_down_exps` routed experts. Mostly `iq4_ks/iq3_ks` for attn and shared expert. `iq4_k` `token_embd` and `iq5_k` `output` "head".

Final estimate: PPL = 4.0078 +/- 0.02291

<details>

<summary>👈 Secret Recipe</summary>

```bash
#!/usr/bin/env bash

custom="
# First 3 dense layers (0-3) (GPU)
# Except blk.*.attn_k_b.weight is not divisible by 256 so only supports qN_0
blk\.[0-2]\.attn_k_b.*=q4_0
blk\.[0-2]\.attn_.*=iq4_ks
blk\.[0-2]\.ffn_down.*=iq4_ks
blk\.[0-2]\.ffn_(gate|up).*=iq3_ks
blk\.[0-2]\..*=iq4_ks

# All attention, norm weights, and bias tensors for MoE layers (3-60) (GPU)
# Except blk.*.attn_k_b.weight is not divisible by 256 so only supports qN_0
blk\.[3-9]\.attn_k_b.*=q4_0
blk\.[1-5][0-9]\.attn_k_b.*=q4_0
blk\.60\.attn_k_b.*=q4_0

blk\.[3-9]\.attn_.*=iq4_ks
blk\.[1-5][0-9]\.attn_.*=iq4_ks
blk\.60\.attn_.*=iq4_ks

# Shared Expert (3-60) (GPU)
blk\.[3-9]\.ffn_down_shexp\.weight=iq4_ks
blk\.[1-5][0-9]\.ffn_down_shexp\.weight=iq4_ks
blk\.60\.ffn_down_shexp\.weight=iq4_ks

blk\.[3-9]\.ffn_(gate|up)_shexp\.weight=iq3_ks
blk\.[1-5][0-9]\.ffn_(gate|up)_shexp\.weight=iq3_ks
blk\.60\.ffn_(gate|up)_shexp\.weight=iq3_ks

# Routed Experts (3-60) (CPU)
blk\.[3-9]\.ffn_down_exps\.weight=iq2_ks
blk\.[1-5][0-9]\.ffn_down_exps\.weight=iq2_ks
blk\.60\.ffn_down_exps\.weight=iq2_ks

blk\.[3-9]\.ffn_(gate|up)_exps\.weight=iq2_xxs
blk\.[1-5][0-9]\.ffn_(gate|up)_exps\.weight=iq2_xxs
blk\.60\.ffn_(gate|up)_exps\.weight=iq2_xxs

# Token embedding and output tensors (GPU)
token_embd\.weight=iq4_k
output\.weight=iq5_k
"

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

./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/DeepSeek-TNG-R1T2-Chimera-GGUF/imatrix-DeepSeek-TNG-R1T2-Chimera-Q8_0.dat \
    /mnt/raid/models/ubergarm/DeepSeek-TNG-R1T2-Chimera-GGUF/DeepSeek-TNG-R1T2-Chimera-256x21B-BF16-00001-of-00030.gguf \
    /mnt/raid/models/ubergarm/DeepSeek-TNG-R1T2-Chimera-GGUF/DeepSeek-TNG-R1T2-Chimera-IQ2_XXS.gguf \
    IQ2_XXS \
    24
```

</details>

#### * `IQ1_S` 132.915 GiB (1.699 BPW)
Not recommended. "For the desperate". If you can fit a larger model in RAM+VRAM choose a larger model as it might even run faster and will definitely have better perplexity (likely better quality).

Special mix `IQ1_S` `ffn_(gate|up)_exps` and `IQ1_M` `ffn_down_exps` routed experts. Mostly `iq4_ks/iq3_ks` for attn and shared expert. `iq4_k` `token_embd` and `iq5_k` `output` "head".

Final estimate: PPL = 4.9878 +/- 0.02999

<details>

<summary>👈 Secret Recipe</summary>

```bash
#!/usr/bin/env bash

custom="
# First 3 dense layers (0-3) (GPU)
# Except blk.*.attn_k_b.weight is not divisible by 256 so only supports qN_0
blk\.[0-2]\.attn_k_b.*=q4_0
blk\.[0-2]\.attn_.*=iq4_ks
blk\.[0-2]\.ffn_down.*=iq4_ks
blk\.[0-2]\.ffn_(gate|up).*=iq3_ks
blk\.[0-2]\..*=iq4_ks

# All attention, norm weights, and bias tensors for MoE layers (3-60) (GPU)
# Except blk.*.attn_k_b.weight is not divisible by 256 so only supports qN_0
blk\.[3-9]\.attn_k_b.*=q4_0
blk\.[1-5][0-9]\.attn_k_b.*=q4_0
blk\.60\.attn_k_b.*=q4_0

blk\.[3-9]\.attn_.*=iq4_ks
blk\.[1-5][0-9]\.attn_.*=iq4_ks
blk\.60\.attn_.*=iq4_ks

# Shared Expert (3-60) (GPU)
blk\.[3-9]\.ffn_down_shexp\.weight=iq4_ks
blk\.[1-5][0-9]\.ffn_down_shexp\.weight=iq4_ks
blk\.60\.ffn_down_shexp\.weight=iq4_ks

blk\.[3-9]\.ffn_(gate|up)_shexp\.weight=iq3_ks
blk\.[1-5][0-9]\.ffn_(gate|up)_shexp\.weight=iq3_ks
blk\.60\.ffn_(gate|up)_shexp\.weight=iq3_ks

# Routed Experts (3-60) (CPU)
blk\.[3-9]\.ffn_down_exps\.weight=iq1_m
blk\.[1-5][0-9]\.ffn_down_exps\.weight=iq1_m
blk\.60\.ffn_down_exps\.weight=iq1_m

blk\.[3-9]\.ffn_(gate|up)_exps\.weight=iq1_s
blk\.[1-5][0-9]\.ffn_(gate|up)_exps\.weight=iq1_s
blk\.60\.ffn_(gate|up)_exps\.weight=iq1_s

# Token embedding and output tensors (GPU)
token_embd\.weight=iq4_k
output\.weight=iq5_k
"

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

./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/DeepSeek-TNG-R1T2-Chimera-GGUF/imatrix-DeepSeek-TNG-R1T2-Chimera-Q8_0.dat \
    /mnt/raid/models/ubergarm/DeepSeek-TNG-R1T2-Chimera-GGUF/DeepSeek-TNG-R1T2-Chimera-256x21B-BF16-00001-of-00030.gguf \
    /mnt/raid/models/ubergarm/DeepSeek-TNG-R1T2-Chimera-GGUF/DeepSeek-TNG-R1T2-Chimera-IQ1_S.gguf \
    IQ1_S \
    24
```

</details>

## Quick Start
```
## clone latest ik_llama.cpp
git clone https://github.com/ikawrakow/ik_llama.cpp.git
cd ik_llama.cpp

## build for hybrid CUDA and CPU DeepSeek inferencing
# apt-get install build-essential cmake ccache nvidia-cuda-toolkit # plus anything you need
cmake -B ./build -DCMAKE_BUILD_TYPE=Release -DGGML_CUDA=ON -DGGML_BLAS=OFF -DGGML_SCHED_MAX_COPIES=1 -DGGML_CUDA_IQK_FORCE_BF16=1
cmake --build ./build --config Release -j $(nproc)

## Run api server
./build/bin/llama-server \
    --model /models/DeepSeek-TNG-R1T2-Chimera-IQ3_KS-00001-of-00007.gguf \
    --alias ubergarm/DeepSeek-TNG-R1T2-Chimera-IQ3_KS \
    -fa \
    -mla 3 -fmoe -amb 512 \
    --ctx-size 32768 \
    -ctk q8_0 \
    -ngl 99 \
    -ot "blk\.(3|4)\.ffn_.*=CUDA0" \
    -ot exps=CPU \
    -ub 1024 -b 2048 \
    --parallel 1 \
    --threads 16 \
    --host 127.0.0.1 \
    --port 8080
```
Adjust `--threads` to be equal to number of physical cores. Refer to various discussions on my other models for multi-NUMA, dual socket, and varying `--threads` and `--threads-batch` for larger server rigs.

If you OOM on VRAM, remove the additional `-ot "...=CUDA0"` or you can increase offload layers if you have more VRAM with multi-GPU targets e.g. `-ot "blk\.(5|6)\.ffn_.*=CUDA1" \`.

Test out `-rtr` to run-time-repack tensors to `_r4` variants layers when running on CPU/RAM likely faster in default ubatch sizes. Note this disables mmap() so will need enough RAM to malloc all the non-offloaded weights on startup.

Generally `-ub 2048 -b 2048` or `-ub 4096 -b 4096` can give *much* faster PP speeds at the cost of some additional VRAM. Test against leavng it at the default `-ub 512 -b 2048`.

Use `llama-sweep-bench --warmup-batch ...` to benchmark various configurations with your hardware to report to the community!

## TODO
- [ ] Given the `IQ1_S_R4` is not symmetric with `IQ1_S` it doesn't work with `-rtr` so I might look into releasing an `_R4` variant after some `llama-sweep-bench` testing.
- [ ] Consider a slightly larger model? (gotta free up some disk space lol)

## References
* [ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp)
* [Larger ik quants available here: Kebob/DeepSeek-TNG-R1T2-Chimera-IK_GGUF](https://huggingface.co/Kebob/DeepSeek-TNG-R1T2-Chimera-IK_GGUF)
* [Getting Started Guide (already out of date lol)](https://github.com/ikawrakow/ik_llama.cpp/discussions/258)