DeepSeek has captured public eyes for months. I decide to deploy it to our local server and make it open to remote usage.

1. Usage (For users)

There are several UI tools.

1.1 Initial test

Type http://101.6.122.12:11434 in your remote device. You should see "Ollama is running", otherwise there are something wrong.

1.2 Use ChatBox

REF ChatBox provide a client and a web. Download the client and install it.

Choose "Use My Own API key / Local Model"
Choose "Ollama API"
Fill API Host with "http://101.6.122.12:11434"
For Model, choose "deepseek-r1:32b"
Click "SAVE"

That's all. Chat with deepseek. You may begin with a "Hello, DeepSeek".

The response may be slow for the first time, since it take some time to wake the model. (冷启动和热启动的区别)

[WARNING!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!]
You may see several different models and be curious about the performance between each other. Don't wake up more than one model at a time! The graphical memory is occupied after one model is waken.

1.3 VSCode

VSCode, along with extension Continue, supports local LLM usage. However, the experience is bad.

I put the configuration file config.json below for convience.

{
  "models": [
    {
      "title": "deepseek-r1:32b",
      "model": "deepseek-r1:32b",
      "provider": "ollama",
      "apiBase": "http://101.6.122.12:11434/"
    }
  ],
  "tabAutocompleteModel": {
      "title": "deepseek-r1:32b",
      "model": "deepseek-r1:32b",
      "provider": "ollama",
      "apiBase": "http://101.6.122.12:11434/"
  }
}

2. Installation (For developers only)

Broadly speaking, use software ollama to download and run the model deepseek, then open a port to make it accessible externally.

2.1 install ollama

REF For linux, download the file install.sh and run it. The software will be installed to path /usr/bin.

curl -fsSL https://ollama.com/install.sh | sh

2.2 change storage path

REF The default storage path is /usr/share, but it's a routine that we put softwares in path /home/usr/share. Thus the next step is move the storage path of ollama.

Make a new directory and change the file permissions

mkdir /home/usr/share/ollama_models
sudo chown -R ollama:ollama /home/usr/share/ollama_models
sudo chmod -R 775 /home/usr/share/ollama_models

Change the default configurations of ollama

sudo vi /etc/systemd/system/ollama.service
# add the next line to [Service]
Environment="OLLAMA_MODELS=/home/usr/share/ollama_models"

Refresh the service of ollama

sudo systemctl daemon-reload
sudo systemctl restart ollama.service
sudo systemctl status ollama

2.3 download deepseek's models

REF The "real" deepseek-r1 is the model with 671 billion parameters. Ollama distilled several models with different file sizes. For 4080 or 3090, deepseek-r1:32b is the most proper one. Smaller ones are worse in performance while bigger ones run slowly.

This is an easy while tricky step. It is easy because just a command ollama run deepseek-r1:32b works. It is tricky because the downloading may restart automatically many many times (REF). There are two ticks:

Download the smallest one to ensure that the pipeline works.
```
ollama run deepseek-r1:1.5b
```

Use a script to download the model. REF

#!/bin/bash
while true; do
  echo "Attempting to download model..."
  ollama pull deepseek-r1:32b &
  process_pid=$!
  sleep 10
  if wait $process_pid; then
      echo "Model downloaded successfully!"
      break
  else
      echo "Download failed. Retrying..."
      kill -9 $process_pid 2>/dev/null
  fi
  sleep 2
done

2.4 make it open to remote access

REF To make the ollama service available externally, you need to set the following two environment variables OLLAMA_HOST and OLLAMA_ORIGINS.

Change the default configuration

sudo vi /etc/systemd/system/ollama.service
# add the two lines to [Service]
Environment="OLLAMA_HOST=0.0.0.0:11434"
Environment="OLLAMA_ORIGINS=*"