DeepSeek w/ Ollama + Open WebUI

Table of Contents

• Table of Contents
• DeepSeek R1 Exists
• Ollama
• Open WebUI
• Putting It All Together

DeepSeek R1 Exists

It’s the latest exciting open-source LLM model and the first (to my knowledge) open-source reasoning model. While I’m unfamiliar with the intricacies of reasoning models, the gist of it is that these LLMs “think through” the problem before responding. In other words, as part of the output that you get from your prompt, you also get the chain of thought that supports the reasoning behind the model’s output. This provides context as to why the model generated its final output.

To be clear, I wouldn’t call these models self-explaining; at the end of the day, LLMs are still considered black boxes that generate text based on statistical and mathematical computations. Just because DeepSeek “thinks through” a problem does not mean that it is truly sentient, accurate, or correct. There is still a need for human-in-the-loop (i.e., human reviewer) style usage when leveraging these models.

With the context and clarification out of the way, how can you leverage DeepSeek R1 locally? And more broadly, how do you do so with any open-source LLM?

Ollama

You leverage Ollama, an open-source inference engine that is designed to work with quantized LLMs via the GGUF file format or hosted on the Ollama Model Hub.

https://media3.giphy.com/media/v1.Y2lkPTc5MGI3NjExOHJuYXpkeHpvN2N1YXFqbmphZTJmaHJpcm1uM2JoNzJ2d3dtdzVzZyZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/3WmWdBzqveXaE/giphy.gif

In short:

• An inference engine is a utility to run machine and deep learning models efficiently by optimizing the model’s underlying computational graph.
• The computational graph is similar to a program’s call graph (or the order in which instructions are executed) but for mathematics
• Quantized LLMs are large language models whose computational graph relies on either a reduced number of bits to represent floating point numbers or integers.
• Deep learning models are often trained using bit widths of 64. 128, or higher to represent the nuances of data that can be represented at a given time. Reducing the bit width or precision (e.g., floating point representation to integer representation) often improves the latency of the model (measured in tokens per second) at the cost of precise answers.
• The GGUF file format is not important for this discussion, but you can learn more about it here
• The Ollama Model Hub hosts quantized LLMs ready for downstream consumption via the ollama command line utility.

Ollama provides a very simple interface to get started with using LLMs locally. Alternatives do exist (e.g., vllm) but the tooling surrounding Ollama is extensive and well-documented, so it is my preferred choice when running LLMs locally.

As Ollama is a command-line utility it can be difficult to leverage tooling such as document and image reasoning, web searching, retrieval augmented generation (RAG), and multi-modal data analysis without having to develop your own interface. This is where GUI interfaces such as Open WebUI fill the gap.

Open WebUI

Open WebUI is a self-hostable application that communicates to Ollama via Ollama’s HTTP REST API. It provides a ChatGPT-like interface that I find familiar while exposing existing ChatGPT features such as image generation, document reasoning, RAG, and web search. It also supports new features like the ability to chain multiple models together to provide one model with a prompt, and then automatically pass the response of that model into a second or third LLM for post-processing! I think it’s a neat project and an exemplar of the Ollama ecosystem. You can find more information about it here.

Putting It All Together

Having gone through all of this now, how can we install these tools?

If you are on an M series Mac, you should install Ollama locally and ignore all references to Ollama docker installation hereafter. This is because Ollama via Docker does not support M series Mac GPU acceleration, but the compiled binary does. You can read about it here.

For everyone else, I recommend installing Ollama and Open Web UI via Docker Compose via this YAML file:

version: '3.8'
name: ai

services:
  ollama:
    container_name: ollama
    image: ollama/ollama:0.5.7
    restart: always
    networks:
      - ollama-network
    ports:
      - "11434:11434"
    volumes:
      - ollama:/root/.ollama
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              capabilities: [gpu]

  open-webui:
    container_name: open-webui
    image: ghcr.io/open-webui/open-webui:0.5.7
    restart: always
    extra_hosts:
      - "host.docker.internal:host-gateway"
    networks:
      - ollama-network
    ports:
      - "3000:8080"
    volumes:
      - open-webui:/app/backend/data

networks:
  ollama-network:
    external: false

volumes:
  ollama:
  open-webui:

Copy this to a docker-compose.yml file and then run:

docker compose --file ./docker-compose.yml create
docker compose --file ./docker-compose.yml start

This installs Ollama at its latest version (as of writing) with NVIDIA GPU acceleration support across all GPUs. It also installs the latest version of Open WebUI (as of writing). The Ollama HTTP REST API is exposed on port 11434 and Open WebUI is exposed on port 3000.

If you don’t have NVIDIA GPU support for Docker or are using a different GPU vendor or intend to run this on CPU, see this post from Ollama.

Once installed run the following command to install DeepSeek R1 from Ollama’s Model Hub:

docker compose --file ./docker-compose.yml exec ollama ollama pull deepseek-r1:7b

Then refresh your browser’s connection to Open WebUI (via http://localhost:3000) and you should be able to start using DeepSeek R1 locally!

NOTE: All computers are different and unique snowflakes. For context, I have deployed this on a system running Pop OS with an NVIDIA 3060 GPU. While I’ve done my best to make deployment of Ollama, Open WebUI, and DeepSeek R1 repeatable and reproducible, your system might need additional tinkering to get it to work right. Please see both the Ollama and Open WebUI documentation and GitHub Issue boards for support.