Grok 2

Run xAI's Grok 2 model locally!

You can now run Grok 2, a 270B parameter model by xAI. Full precision requires 539GB, while the Unsloth Dynamic 3-bit version shrinks size down to just 118GB (a 75% reduction). GGUF: Grok-2-GGUF

Thanks to the llama.cpp team and community for supporting Grok 2 and making this possible. We were also glad to have helped a little along the way. The 3-bit Q3_K_XL model runs on a single 128GB Mac or 24GB VRAM + 128GB RAM, achieving 5+ tokens/s inference.

All uploads use Unsloth Dynamic 2.0 for SOTA 5-shot MMLU and KL Divergence performance, meaning you can run quantized Grok LLMs with minimal accuracy loss.

Run in llama.cpp Tutorial

⚙️ Recommended Settings

The 3-bit dynamic quant uses 118GB (126GiB) of disk space - this works well in a 128GB RAM unified memory Mac or on a 1x24GB card and 128GB of RAM. It is recommended to have at least 120GB RAM to run this 3-bit quant.

You must use --jinja for Grok 2. You might get incorrect results if you do not use --jinja

The 8-bit quant is ~300GB in size will fit in a 1x 80GB GPU (with MoE layers offloaded to RAM). Expect around 5 tokens/s with this setup if you have bonus 200GB RAM as well. To learn how to increase generation speed and fit longer contexts, read here.

Though not a must, for best performance, have your VRAM + RAM combined equal to the size of the quant you're downloading. If not, hard drive / SSD offloading will work with llama.cpp, just inference will be slower.

Sampling parameters

• Grok 2 has a 128K max context length thus, use 131,072 context or less.

• Use --jinja for llama.cpp variants

There are no official sampling parameters to run the model, thus you can use standard defaults for most models:

• Set the temperature = 1.0

• Min_P = 0.01 (optional, but 0.01 works well, llama.cpp default is 0.1)

Run Grok 2 Tutorial:

Currently you can only run Grok 2 in llama.cpp.

✨ Run in llama.cpp

1

Obtain the specific llama.cpp PR for Grok 2 on GitHub here. You can follow the build instructions below as well. Change -DGGML_CUDA=ON to -DGGML_CUDA=OFF if you don't have a GPU or just want CPU inference.

apt-get update
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
git clone https://github.com/ggml-org/llama.cpp
git fetch origin pull/15539/head:MASTER && git checkout MASTER
cmake llama.cpp -B llama.cpp/build \
    -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON -DLLAMA_CURL=ON
cmake --build llama.cpp/build --config Release -j --clean-first --target llama-quantize llama-cli llama-gguf-split llama-mtmd-cli llama-server
cp llama.cpp/build/bin/llama-* llama.cpp

2

If you want to use llama.cpp directly to load models, you can do the below: (:Q3_K_XL) is the quantization type. You can also download via Hugging Face (point 3). This is similar to ollama run . Use export LLAMA_CACHE="folder" to force llama.cpp to save to a specific location. Remember the model has only a maximum of 128K context length.

Please try out -ot ".ffn_.*_exps.=CPU" to offload all MoE layers to the CPU! This effectively allows you to fit all non MoE layers on 1 GPU, improving generation speeds. You can customize the regex expression to fit more layers if you have more GPU capacity.

If you have a bit more GPU memory, try -ot ".ffn_(up|down)_exps.=CPU" This offloads up and down projection MoE layers.

Try -ot ".ffn_(up)_exps.=CPU" if you have even more GPU memory. This offloads only up projection MoE layers.

And finally offload all layers via -ot ".ffn_.*_exps.=CPU" This uses the least VRAM.

You can also customize the regex, for example -ot "\.(6|7|8|9|[0-9][0-9]|[0-9][0-9][0-9])\.ffn_(gate|up|down)_exps.=CPU" means to offload gate, up and down MoE layers but only from the 6th layer onwards.

export LLAMA_CACHE="unsloth/grok-2-GGUF"
./llama.cpp/llama-cli \
    -hf unsloth/grok-2-GGUF:Q3_K_XL \
    --jinja \
    --n-gpu-layers 99 \
    --temp 1.0 \
    --top_p 0.95 \
    --min_p 0.01 \
    --ctx-size 16384 \
    --seed 3407 \
    -ot ".ffn_.*_exps.=CPU"

3

Download the model via (after installing pip install huggingface_hub hf_transfer ). You can choose UD-Q3_K_XL (dynamic 3-bit quant) or other quantized versions like Q4_K_M . We recommend using our 2.7bit dynamic quant UD-Q2_K_XL or above to balance size and accuracy.

# !pip install huggingface_hub hf_transfer
import os
os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "0" # Can sometimes rate limit, so set to 0 to disable
from huggingface_hub import snapshot_download
snapshot_download(
    repo_id = "unsloth/grok-2-GGUF",
    local_dir = "unsloth/grok-2-GGUF",
    allow_patterns = ["*UD-Q3_K_XL*"], # Dynamic 3bit
)

4

You can edit --threads 32 for the number of CPU threads, --ctx-size 16384 for context length, --n-gpu-layers 2 for GPU offloading on how many layers. Try adjusting it if your GPU goes out of memory. Also remove it if you have CPU only inference.

./llama.cpp/llama-cli \
    --model unsloth/grok-2-GGUF/UD-Q3_K_XL/grok-2-UD-Q3_K_XL-00001-of-00003.gguf \
    --jinja \
    --threads -1 \
    --n-gpu-layers 99 \
    --temp 1.0 \
    --top_p 0.95 \
    --min_p 0.01 \
    --ctx-size 16384 \
    --seed 3407 \
    -ot ".ffn_.*_exps.=CPU"

Model uploads

ALL our uploads - including those that are not imatrix-based or dynamic, utilize our calibration dataset, which is specifically optimized for conversational, coding, and language tasks.

MoE Bits	Type + Link	Disk Size	Details
1.66bit	TQ1_0	81.8 GB	1.92/1.56bit
1.78bit	IQ1_S	88.9 GB	2.06/1.56bit
1.93bit	IQ1_M	94.5 GB	2.5/2.06/1.56
2.42bit	IQ2_XXS	99.3 GB	2.5/2.06bit
2.71bit	Q2_K_XL	112 GB	3.5/2.5bit
3.12bit	IQ3_XXS	117 GB	3.5/2.06bit
3.5bit	Q3_K_XL	126 GB	4.5/3.5bit
4.5bit	Q4_K_XL	155 GB	5.5/4.5bit
5.5bit	Q5_K_XL	191 GB	6.5/5.5bit

🏂 Improving generation speed

If you have more VRAM, you can try offloading more MoE layers, or offloading whole layers themselves.

Normally, -ot ".ffn_.*_exps.=CPU" offloads all MoE layers to the CPU! This effectively allows you to fit all non MoE layers on 1 GPU, improving generation speeds. You can customize the regex expression to fit more layers if you have more GPU capacity.

If you have a bit more GPU memory, try -ot ".ffn_(up|down)_exps.=CPU" This offloads up and down projection MoE layers.

Try -ot ".ffn_(up)_exps.=CPU" if you have even more GPU memory. This offloads only up projection MoE layers.

You can also customize the regex, for example -ot "\.(6|7|8|9|[0-9][0-9]|[0-9][0-9][0-9])\.ffn_(gate|up|down)_exps.=CPU" means to offload gate, up and down MoE layers but only from the 6th layer onwards.

The latest llama.cpp release also introduces high throughput mode. Use llama-parallel. Read more about it here. You can also quantize the KV cache to 4bits for example to reduce VRAM / RAM movement, which can also make the generation process faster.

📐How to fit long context (full 128K)

To fit longer context, you can use KV cache quantization to quantize the K and V caches to lower bits. This can also increase generation speed due to reduced RAM / VRAM data movement. The allowed options for K quantization (default is f16) include the below.

--cache-type-k f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1

You should use the _1 variants for somewhat increased accuracy, albeit it's slightly slower. For eg q4_1, q5_1

You can also quantize the V cache, but you will need to compile llama.cpp with Flash Attention support via -DGGML_CUDA_FA_ALL_QUANTS=ON, and use --flash-attn to enable it. Then you can use together with --cache-type-k :

--cache-type-v f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1