Ollama provides an interface to self-host and interact with open-source LLMs (Large Language Models) using its binary or container image. Managing LLMs using Ollama is like managing container lifecycle using container engines like docker or podman.

• Ollama commands pull and run are used to download and execute LLMs respectively, just like the ones used to manage containers with podman or docker.

• Tags like 13b-python and 7b-code are used to manage different variations of an LLM.

• A Modelfile (like Dockerfile) is created to build a custom model using an existing LLM as its base. Additional parameters like TEMPLATE and PARAMETER could be used to define a prompt template or fine-tune model parameters respectively.

Deploying Ollama container with NVIDIA GPU

Deploying the Ollama container directly would allow it to utilize CPU resources for its LLM workloads, but with the parallel computation capabilities of a Graphics Processing Unit (GPU), we can improve the inference performance of all models.

In this article I’m using an NVIDIA GeForce RTX 3070 Ti GPU, if you want to use a GPU from AMD/Intel or any other manufacturer then steps like driver and container toolkit installation and GPU configuration for the container engine will differ.

GPU Passthrough to VM

I am deploying the Ollama container on a Fedora 38 virtual machine so the first step will be the GPU Passthrough from my hypervisor (Proxmox) to the VM. You can skip this step if you are deploying Ollama on a baremetal machine.

In the Proxmox’s Web UI, we can go to the VM’s Hardware section and Add your PCI Device i.e. your GPU.

Proxmox VM's Hardware Section

Make sure to mark the All Functions checkbox.

GPU Passthrough to a Proxmox VM

Once the VM is rebooted we can verify the GPU Passthrough using the following command.

lspci | grep NVIDIA

If the GPU name is present in the command’s output (like below) then the passthrough is successful and we can move to the next step.

06:10.0 VGA compatible controller: NVIDIA Corporation GA104 [GeForce RTX 3070 Ti] (rev a1)
06:10.1 Audio device: NVIDIA Corporation GA104 High Definition Audio Controller (rev a1)

CUDA Toolkit Installation

To utilize the parallel computation capabilities of the CUDA cores provided in NVIDIA GPUs we have to install the CUDA Toolkit. You can follow NVIDIA’s documentation on the CUDA Toolkit installation on Linux because the steps vary depending on the host’s configuration.

Here are the steps for Fedora 38:

1. Downloading CUDA Toolkit Repo RPM.

wget https://developer.download.nvidia.com/compute/cuda/12.3.2/local_installers/cuda-repo-fedora37-12-3-local-12.3.2_545.23.08-1.x86_64.rpm

2. Installing CUDA Toolkit Repo RPM.

sudo rpm -i cuda-repo-fedora37-12-3-local-12.3.2_545.23.08-1.x86_64.rpm

3. Cleaning dnf Repository Metadata.

sudo dnf clean all

4. Installing cuda-toolkit package.

sudo dnf -y install cuda-toolkit-12-3

5. Installing legacy (proprietary) or open (open source) kernel module for nvidia-driver.

sudo dnf -y module install nvidia-driver:latest-dkms

or

sudo dnf -y module install nvidia-driver:open-dkms

NVIDIA Container Toolkit Installation

With nvidia-container-tookit, we can use our NVIDIA GPU in containerized applications. Here are the steps for installing NVIDIA Container Toolkit on Fedora 38:

1. Adding nvidia-container-tookit repository.

curl -s -L https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo \
    | sudo tee /etc/yum.repos.d/nvidia-container-toolkit.repo

2. Installing the nvidia-container-tookit package.

sudo dnf install -y nvidia-container-toolkit

3. Once the container toolkit is installed, we have to add its runtime to our container engine.

sudo nvidia-ctk runtime configure --runtime=docker

4. Finally, we can start using our NVIDIA GPU with Docker containers after restarting the docker Daemon.

sudo systemctl restart docker

Deploying Ollama as a Docker Container

1. Create a directory on our host to store LLMs to avoid re-downloading models after reprovisioning or updating the container.

mkdir -p ~/container-data/ollama

2. The following compose.yaml file will deploy the ollama container with our NVIDIA GPU.

version: '3.6'

services:
  ollama:
    container_name: ollama
    image: ollama/ollama:latest
    volumes:
      - ~/container-data/ollama:/root/.ollama
    ports:
      - "11434:11434"
    restart: unless-stopped
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]

If you want to provision the container without GPU you have to remove the deploy section.

3. Deploy the ollama container using the following command.

docker compose down && docker compose up -d

If you want to deploy Ollama with a ChatGPT-Style Web UI then follow the deployment steps for Ollama Web UI.

Managing LLMs using Ollama

Once the container is provisioned we can start downloading and executing models.

To attach the Ollama container with a terminal use the following command

docker exec -it ollama /bin/bash

Downloading LLMs using the pull command

To download a model use the ollama pull command with the name of LLM and its tag (refer to the Ollama Library).

For example, to download the Code Llama model with 7 Billion parameters we have to pull the codellama:7b model.

ollama pull codellama:7b

The model size could range from 4 to 19 GB (or even more). So choosing the right model tag is crucial to decrease downloading time and resource utilization.

If we want to delete a downloaded model we’ll use the ollama rm command followed by the name of the model.

Executing LLMs using the run command

Before we prompt the model we have to run it first using the ollama run command followed by the name of the model.

ollama run codellama:7b

This command will drop us directly into the model’s prompting window.

>>> Who are you?
I am LLaMA, an AI assistant developed by Meta AI that 
can understand and respond to human input in a conversational 
manner. I am trained on a massive dataset of text from the 
internet and can generate human-like responses to a wide range 
of topics and questions. I can be used to create chatbots, 
virtual assistants, and other applications that require natural 
language understanding and generation capabilities.

Prompting LLMs from Command Line

Ollama exposes multiple REST API endpoints to manage and interact with the models

• /api/tags: To list all the local models.
• /api/generate: To generate a response from an LLM with the prompt passed as input.
• /api/chat: To generate the next chat response from an LLM. The prior chat history could be passed as input.

We can perform these API requests using curl and format the response using jq.

curl -d '{
  "model": "codellama:7b",
  "prompt": "Write a quicksort program in Go",
  "stream": false
}' http://localhost:11434/api/generate | jq -r ".response"

By assigning the stream as false we will receive the complete response as a single JSON object rather than a stream of multiple objects.

Ollama Web UI

With self-hosted applications, it always helps to have a web interface for management and access from any device. The Ollama Web UI provides an interface similar to ChatGPT to interact with LLMs present in Ollama.

Deploying Ollama Web UI

Similar to the ollama container deployment we will create a data directory for ollama-webui

mkdir -p ~/container-data/ollama-webui

Modify our existing compose.yaml.

version: '3.6'

services:
  ollama:
    container_name: ollama
    image: ollama/ollama:latest
    volumes:
      - ~/container-data/ollama:/root/.ollama
    ports:
      - "11434:11434"
    restart: unless-stopped
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]
  ollama-webui:
    container_name: ollama-webui
    image: ghcr.io/ollama-webui/ollama-webui:main
    ports:
        - "3030:8080"
    extra_hosts:
        - host.docker.internal:host-gateway
    volumes:
        - ~/container-data/ollama-webui:/app/backend/data
    restart: always

Deploy both containers using docker compose.

docker compose down && docker compose up -d

If the ollama container is deployed on a different host then we have to rebuild the ollama-webui container image by following the instructions from here.

Managing LLMs from Ollama Web UI

Once the deployment is completed we can visit the web UI at localhost:3030.

Ollama Web UI

Alongside prompting we can also use the Web UI to manage models.

Managing models using Ollama Web UI

Integrating Ollama with Neovim

If you are using Neovim (like me) then you can integrate models in your development environment using ollama.nvim.

ollama.nvim supports the following features:

• Code generation from a text prompt
• Generating an explanation for a code snippet
• Code modification suggestions

Code explanation from Ollama using ollama.nvim

I am using LazyVim so I’ve created ~/.config/nvim/lua/plugins/ollama.lua with the following content.

return {
	"nomnivore/ollama.nvim",
	dependencies = {
		"nvim-lua/plenary.nvim",
	},
	-- All the user commands added by the plugin
	cmd = { "Ollama", "OllamaModel", "OllamaServe", "OllamaServeStop" },
	keys = {
		-- Sample keybind for prompt menu.
		-- Note that the <c-u> is important for selections
		-- to work properly.
		{
			"<leader>oo",
			":<c-u>lua require('ollama').prompt()<cr>",
			desc = "ollama prompt",
			mode = { "n", "v" },
		},
		-- Sample keybind for direct prompting.
		-- Note that the <c-u> is important for selections
		-- to work properly.
		{
			"<leader>oG",
			":<c-u>lua require('ollama').prompt('Generate_Code')<cr>",
			desc = "ollama Generate Code",
			mode = { "n", "v" },
		},
	},
	---@type Ollama.Config
	opts = {
		model = "codellama:7b",
		url = "http://127.0.0.1:11434",
	},
}

Integrating Ollama with VSCode

The Continue VSCode extension supports the integration of LLMs as coding assistants. To use it with Ollama we have to change the Proxy Server Url in its settings to the one used by our Ollama container.

Continue Extension Settings

Watch Ollama in action inside VSCode

Optimizing code using Ollama in VSCode

Resources

Ollama
Ollama Docker Image
NVIDIA GeForce RTX 3070 Ti
NVIDIA CUDA Installation Guide for Linux
NVIDIA Container Toolkit
Ollama Library
Ollama Web UI
ollama.nvim
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