How to Run and Fine-Tune Kimi K2 Thinking Locally with Unsloth

The demand for efficient and powerful large language models (LLMs) continues to rise as developers and researchers seek new ways to optimize reasoning, coding, and conversational AI performance. One of the most impressive open-source AI systems available today is Kimi K2 Thinking, created by Moonshot AI. Through collaboration with Unsloth, users can now fine-tune and deploy this model locally using llama.cpp, offering powerful customization and reduced infrastructure costs.

This detailed guide will walk you through everything you need to know about running and fine-tuning Kimi K2 Thinking with Unsloth from system requirements and quantization formats to installation steps and performance optimization.

Introduction to Kimi K2 Thinking

Kimi K2 Thinking is a next-generation model developed by Moonshot AI, known for its advanced reasoning and high-performance coding capabilities. It represents a significant leap forward in open-source AI due to its state-of-the-art (SOTA) results across multiple benchmarks including MMLU and Aider Polyglot tasks.

The model comes in two primary versions: Kimi-K2-Thinking and Kimi-K2-Instruct. While both are powerful, the “Thinking” variant focuses on reasoning and cognitive task performance. Thanks to Unsloth, these models have been optimized into compact GGUF quantized formats that dramatically reduce file size while maintaining near-original accuracy.

Why Use Unsloth for Fine-Tuning?

Unsloth simplifies the process of fine-tuning large models such as Kimi K2, DeepSeek and Qwen by offering pre-quantized versions, Docker integration, and reinforcement learning compatibility. Its Dynamic 2.0 quantization method allows high compression ratios (1–2 bits) without significantly sacrificing accuracy.

For example, while the full 1T parameter version of Kimi-K2-Thinking requires around 1.09 TB, Unsloth’s 1.8-bit GGUF quantization reduces it to about 230 GB, an impressive 80% reduction in size. This makes local inference practical even for individuals with consumer-grade hardware.

Recommended Hardware Requirements

Running Kimi K2 Thinking locally depends heavily on available RAM, VRAM, and disk capacity.

Here are the recommended configurations:

Minimum disk space: 247 GB
Combined RAM + VRAM: At least 247 GB for smooth operation
GPU requirement: 24 GB GPU (can offload MoE layers to system RAM or disk)
Performance expectation: Around 1–2 tokens per second on limited setups; 5+ tokens per second on high-end configurations

If your system falls short of these requirements, llama.cpp’s built-in disk offloading (mmap) feature ensures that the model still runs, though at a slower speed. Users are advised to use UD-Q2_K_XL (360GB) quant for the best balance between performance and size.

Installing and Running Kimi K2 Thinking with llama.cpp

To run the model locally, you need to compile llama.cpp and download the required quantized files from Hugging Face.

Step 1: Install llama.cpp

apt-get update

apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y

git clone https://github.com/ggml-org/llama.cpp

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

cp llama.cpp/build/bin/llama-* llama.cpp

If you don’t have a GPU, change -DGGML_CUDA=ON to -DGGML_CUDA=OFF for CPU-only inference.

Step 2: Download the Model

Install the required Python packages and download the Unsloth Kimi model from Hugging Face:

pip install huggingface_hub hf_transfer

from huggingface_hub import snapshot_download

snapshot_download(

    repo_id = "unsloth/Kimi-K2-Thinking-GGUF",

    local_dir = "unsloth/Kimi-K2-Thinking-GGUF",

    allow_patterns = ["*UD-TQ1_0*"]

)

If you prefer higher precision, you can replace UD-TQ1_0 with UD-Q2_K_XL for 2-bit quantization.

Step 3: Run the Model

Once the download is complete, you can launch inference using:

./llama.cpp/llama-cli \

    --model unsloth/Kimi-K2-Thinking-GGUF/UD-TQ1_0/Kimi-K2-Thinking-UD-TQ1_0-00001-of-00006.gguf \

    --n-gpu-layers 99 \

    --temp 1.0 \

    --min_p 0.01 \

    --ctx-size 16384 \

    --seed 3407 \

    -ot ".ffn_.*_exps.=CPU"

This command runs the model efficiently by offloading mixture-of-experts (MoE) layers to the CPU, saving VRAM. Adjust –n-gpu-layers depending on your GPU memory to prevent out-of-memory errors.

Running as an API Server

You can also deploy Kimi K2 Thinking as a local OpenAI-compatible API. After building llama.cpp, run:

./llama.cpp/llama-server \

    --model unsloth/Kimi-K2-Thinking-GGUF/UD-TQ1_0/Kimi-K2-Thinking-UD-TQ1_0-00001-of-00006.gguf \

    --alias "unsloth/Kimi-K2-Thinking" \

    --threads -1 \

    --n-gpu-layers 99 \

    -ot ".ffn_.*_exps.=CPU" \

    --port 8001 \

    --jinja

Then connect via Python using:

from openai import OpenAI

client = OpenAI(base_url="http://127.0.0.1:8001/v1", api_key="sk-no-key-required")

completion = client.chat.completions.create(

    model="unsloth/Kimi-K2-Thinking",

    messages=[{"role": "user", "content": "What is 2+2?"}]

)

print(completion.choices[0].message.content)

This setup allows seamless local integration with your applications while preserving OpenAI-compatible behavior.

Conclusion

Kimi K2 Thinking, fine-tuned and optimized with Unsloth, offers an unprecedented opportunity to run a state-of-the-art AI model locally. With its advanced reasoning capabilities, compact quantized formats, and flexible hardware support, this model bridges the gap between open-source innovation and large-scale AI performance. Whether you’re a researcher, developer, or AI enthusiast, running Kimi K2 Thinking with Unsloth provides the perfect foundation for experimentation, deployment and exploration in the world of generative AI.

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References

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