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Run DeepSeek-V3.1 Dynamic 1-bit GGUFs

Aug 21, 2025 • By Daniel & Michael

Aug 21, 2025

By Daniel & Michael

DeepSeek-V3,1 is DeepSeek's new update to their V3 models. V3.1 is a hybrid reasoning model, rivalling OpenAI's GPT-4.5, o3 and Google's Gemini 2.5 Pro.

You can run the model using Unsloth's 2-bit Dynamic 2.0 GGUFs on your favorite inference frameworks. We quantized DeepSeek-V3.1, a 671B parameter model from 720GB down to 245GB - a 75% size reduction.

Recommended: Read our Complete Guide for a walkthrough on how to run DeepSeek-V3.1 locally.

To ensure the best tradeoff between accuracy and size, we do not to quantize all layers, but selectively quantize e.g. the MoE layers to lower bit, and leave attention and other layers in 4 or 6bit.

And see our DeepSeek-V3.1 GGUFs here
🐋How to Run DeepSeek-V3.1
DeepSeek’s V3.1 update introduces hybrid reasoning inference, combining 'think' and 'non-think' into one model. The full 671B parameter model requires 715GB of disk space. The quantized dynamic 2-bit version uses 245GB (-80% reduction in size).

According to DeepSeek, these are the recommended settings for V3.1 inference:
- Set the temperature 0.6to reduce repetition and incoherence.
- Set top_p to 0.95(recommended)
- 128K context length or less

🦙 How to V3.1 in Ollama:

  • Install ollama if you haven't already!
  • apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh
  • If you want to use any of the quants that are larger on Ollama, you need to first merge the 3 GGUF split files into 1 like the code below. Then you will need to run the model locally.
  • ./llama.cpp/llama-gguf-split --merge \ DeepSeek-V3.1-GGUF/DeepSeek-V3.1-UD-Q2_K_XL/DeepSeek-V3.1-UD-Q2_K_XL-00001-of-00006.gguf \ merged_file.gguf
  • Run the model! Note you can call ollama serve in another terminal if it fails! We include all our fixes and suggested parameters (temperature etc) in params in our Hugging Face upload!
  • OLLAMA_MODELS=unsloth_downloaded_models ollama serve & ollama run hf.co/unsloth/DeepSeek-V3.1-GGUF:Q2_K_XL

✨ How to Run V3.1 in llama.cpp:

  • Obtain the latest llama.cpp 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 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 cp llama.cpp/build/bin/llama-* llama.cpp
  • If you want to use llama.cpp directly to load models, you can do the below: (:Q2_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.
  • export LLAMA_CACHE="unsloth/DeepSeek-V3.1-GGUF" ./llama.cpp/llama-cli \ -hf unsloth/DeepSeek-V3.1-GGUF:Q2_K_XL \ --cache-type-k q4_0 \ --threads -1 \ --n-gpu-layers 99 \ --prio 3 \ --temp 0.6 \ --top_p 0.95 \ --min_p 0.01 \ --ctx-size 16384 \ --seed 3407 \ -ot ".ffn_.*_exps.=CPU"
  • Download the model via (after installing pip install huggingface_hub hf_transfer ). You can choose UD-Q2_K_XL (dynamic 2-bit quant) or other quantized versions like Q4_K_M . I recommend using our 2.7bit dynamic quant UD-Q2_K_XL 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/DeepSeek-V3.1-GGUF", local_dir = "unsloth/DeepSeek-V3.1-GGUF", allow_patterns = ["*UD-Q2_K_XL*"], # Dynamic 1bit (168GB) Use "*UD-Q2_K_XL*" for Dynamic 2bit (251GB) )
  • Run the model by prompting it.
    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/DeepSeek-V3.1-GGUF/UD-Q2_K_XL/DeepSeek-V3.1-UD-Q2_K_XL-00001-of-00006.gguf \ --cache-type-k q4_0 \ --threads -1 \ --n-gpu-layers 99 \ --prio 3 \ --temp 0.6 \ --top_p 0.95 \ --min_p 0.01 \ --ctx-size 16384 \ --seed 3407 \ -ot ".ffn_.*_exps.=CPU" \ -no-cnv \ --prompt "<|User|>Create a Flappy Bird game in Python. You must include these things:\n1. You must use pygame.\n2. The background color should be randomly chosen and is a light shade. Start with a light blue color.\n3. Pressing SPACE multiple times will accelerate the bird.\n4. The bird's shape should be randomly chosen as a square, circle or triangle. The color should be randomly chosen as a dark color.\n5. Place on the bottom some land colored as dark brown or yellow chosen randomly.\n6. Make a score shown on the top right side. Increment if you pass pipes and don't hit them.\n7. Make randomly spaced pipes with enough space. Color them randomly as dark green or light brown or a dark gray shade.\n8. When you lose, show the best score. Make the text inside the screen. Pressing q or Esc will quit the game. Restarting is pressing SPACE again.\nThe final game should be inside a markdown section in Python. Check your code for errors and fix them before the final markdown section.<|Assistant|>"
  • We also test our dynamic quants via the Heptagon test which tests the model on creating a basic physics engine to simulate balls rotating in a moving enclosed heptagon shape.
./llama.cpp/llama-cli \ --model unsloth/DeepSeek-V3.1-GGUF/UD-Q2_K_XL/DeepSeek-V3.1-UD-Q2_K_XL-00001-of-00006.gguf \ --cache-type-k q4_0 \ --threads -1 \ --n-gpu-layers 99 \ --prio 3 \ --temp 0.6 \ --top_p 0.95 \ --min_p 0.01 \ --ctx-size 16384 \ --seed 3407 \ -ot ".ffn_.*_exps.=CPU" \ -no-cnv \ --prompt "<|User|>Write a Python program that shows 20 balls bouncing inside a spinning heptagon:\n- All balls have the same radius.\n- All balls have a number on it from 1 to 20.\n- All balls drop from the heptagon center when starting.\n- Colors are: #f8b862, #f6ad49, #f39800, #f08300, #ec6d51, #ee7948, #ed6d3d, #ec6800, #ec6800, #ee7800, #eb6238, #ea5506, #ea5506, #eb6101, #e49e61, #e45e32, #e17b34, #dd7a56, #db8449, #d66a35\n- The balls should be affected by gravity and friction, and they must bounce off the rotating walls realistically. There should also be collisions between balls.\n- The material of all the balls determines that their impact bounce height will not exceed the radius of the heptagon, but higher than ball radius.\n- All balls rotate with friction, the numbers on the ball can be used to indicate the spin of the ball.\n- The heptagon is spinning around its center, and the speed of spinning is 360 degrees per 5 seconds.\n- The heptagon size should be large enough to contain all the balls.\n- Do not use the pygame library; implement collision detection algorithms and collision response etc. by yourself. The following Python libraries are allowed: tkinter, math, numpy, dataclasses, typing, sys.\n- All codes should be put in a single Python file.<|Assistant|>"
💕 Thank you! 
Thank you for the constant support. We hope to have some great news in the coming weeks! 🙏

As always, be sure to join our Reddit page and Discord server for help or just to show your support! You can also follow us on Twitter and newsletter.
Thank you for reading!
Daniel & Michael Han 🦥
21 Aug 2025

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