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🦙Starting with Llama.cpp

Overview​

Open WebUI makes it simple and flexible to connect and manage a local Llama.cpp server to run efficient, quantized language models. Whether you’ve compiled Llama.cpp yourself or you're using precompiled binaries, this guide will walk you through how to:

  • Set up your Llama.cpp server
  • Load large models locally
  • Integrate with Open WebUI for a seamless interface

Let’s get you started!

Step 1: Install Llama.cpp​

To run models with Llama.cpp, you first need the Llama.cpp server installed locally.

You can either:

After installing, make sure llama-server is available in your local system path or take note of its location.

Step 2: Download a Supported Model​

You can load and run various GGUF-format quantized LLMs using Llama.cpp. One impressive example is the DeepSeek-R1 1.58-bit model optimized by UnslothAI. To download this version:

  1. Visit the Unsloth DeepSeek-R1 repository on Hugging Face
  2. Download the 1.58-bit quantized version – around 131GB.

Alternatively, use Python to download programmatically:

# pip install huggingface_hub hf_transfer

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id = "unsloth/DeepSeek-R1-GGUF",
    local_dir = "DeepSeek-R1-GGUF",
    allow_patterns = ["*UD-IQ1_S*"],  # Download only 1.58-bit variant
)

This will download the model files into a directory like:

DeepSeek-R1-GGUF/
└── DeepSeek-R1-UD-IQ1_S/
    ├── DeepSeek-R1-UD-IQ1_S-00001-of-00003.gguf
    ├── DeepSeek-R1-UD-IQ1_S-00002-of-00003.gguf
    └── DeepSeek-R1-UD-IQ1_S-00003-of-00003.gguf

📍 Keep track of the full path to the first GGUF file — you’ll need it in Step 3.

Step 3: Serve the Model with Llama.cpp​

Start the model server using the llama-server binary. Navigate to your llama.cpp folder (e.g., build/bin) and run:

./llama-server \
  --model /your/full/path/to/DeepSeek-R1-UD-IQ1_S-00001-of-00003.gguf \
  --port 10000 \
  --ctx-size 1024 \
  --n-gpu-layers 40

🛠️ Tweak the parameters to suit your machine:

  • --model: Path to your .gguf model file
  • --port: 10000 (or choose another open port)
  • --ctx-size: Token context length (can increase if RAM allows)
  • --n-gpu-layers: Layers offloaded to GPU for faster performance

Once the server runs, it will expose a local OpenAI-compatible API on:

http://127.0.0.1:10000

Step 4: Connect Llama.cpp to Open WebUI​

To control and query your locally running model directly from Open WebUI:

  1. Open Open WebUI in your browser
  2. Go to ⚙️ Admin Settings → Connections → OpenAI Connections
  3. Click ➕ Add Connection and enter:
  • URL: http://127.0.0.1:10000/v1
    (Or use http://host.docker.internal:10000/v1 if running WebUI inside Docker)
  • API Key: none (leave blank)

💡 Once saved, Open WebUI will begin using your local Llama.cpp server as a backend!

Llama.cpp Connection in Open WebUI

Quick Tip: Try Out the Model via Chat Interface​

Once connected, select the model from the Open WebUI chat menu and start interacting!

Model Chat Preview

You're Ready to Go!​

Once configured, Open WebUI makes it easy to:

  • Manage and switch between local models served by Llama.cpp
  • Use the OpenAI-compatible API with no key needed
  • Experiment with massive models like DeepSeek-R1 — right from your machine!

🚀 Have fun experimenting and building!