ubuntu-ollama

name	ubuntu-ollama
description	Ubuntu Ollama — run Ollama on Ubuntu with fleet routing across multiple Ubuntu machines. Ubuntu Ollama setup with apt, systemd, and NVIDIA CUDA. Route Ollama inference across Ubuntu servers and desktops. Ubuntu Ollama load balancing, auto-discovery, and health monitoring. Ubuntu Ollama本地推理。Ubuntu Ollama enrutador IA.
version	1.0.0
homepage	https://github.com/geeks-accelerator/ollama-herd
metadata	{"openclaw":{"emoji":"penguin","requires":{"anyBins":["curl","wget"],"optionalBins":["python3","pip","nvidia-smi","systemctl","apt"]},"configPaths":["~/.fleet-manager/latency.db","~/.fleet-manager/logs/herd.jsonl"],"os":["linux"]}}

Ubuntu Ollama — Fleet Routing for Ollama on Ubuntu

Run Ollama on Ubuntu with multi-machine load balancing. Ubuntu Ollama Herd turns your Ubuntu servers and desktops into one smart Ollama endpoint. Install with apt + pip, manage with systemd, monitor with the web dashboard.

Ubuntu Ollama setup

Step 1: Install Ollama on Ubuntu

# Install Ollama on Ubuntu
curl -fsSL https://ollama.ai/install.sh | sh

# Verify Ollama is running on Ubuntu
ollama --version
systemctl status ollama

Step 2: Install Ubuntu Ollama Herd

# Ubuntu prerequisites
sudo apt update && sudo apt install python3-pip curl -y

# Install Ubuntu Ollama fleet router
pip install ollama-herd

Step 3: Start Ubuntu Ollama router

On one Ubuntu machine (the router):

herd          # start Ubuntu Ollama router on port 11435
herd-node     # register this Ubuntu Ollama node

On every other Ubuntu machine:

herd-node     # auto-discovers the Ubuntu Ollama router via mDNS

No mDNS? Connect Ubuntu Ollama nodes directly: herd-node --router-url http://router-ip:11435

Step 4: Verify Ubuntu Ollama fleet

curl -s http://localhost:11435/fleet/status | python3 -m json.tool

Ubuntu Ollama systemd services

Run Ubuntu Ollama as systemd services for automatic startup:

# Ubuntu Ollama router service
sudo tee /etc/systemd/system/herd-router.service << 'EOF'
[Unit]
Description=Ubuntu Ollama Router
After=network.target ollama.service

[Service]
Type=simple
ExecStart=/usr/local/bin/herd
Restart=always
RestartSec=5

[Install]
WantedBy=multi-user.target
EOF

# Ubuntu Ollama node service
sudo tee /etc/systemd/system/herd-node.service << 'EOF'
[Unit]
Description=Ubuntu Ollama Node
After=network.target ollama.service

[Service]
Type=simple
ExecStart=/usr/local/bin/herd-node
Restart=always
RestartSec=5

[Install]
WantedBy=multi-user.target
EOF

sudo systemctl enable --now herd-router
sudo systemctl enable --now herd-node

Use Ubuntu Ollama

OpenAI SDK

from openai import OpenAI

# Your Ubuntu Ollama fleet
client = OpenAI(base_url="http://localhost:11435/v1", api_key="not-needed")

response = client.chat.completions.create(
    model="llama3.3:70b",
    messages=[{"role": "user", "content": "Write an Ubuntu cron job for log rotation"}],
    stream=True,
)
for chunk in response:
    print(chunk.choices[0].delta.content or "", end="")

curl (Ollama format)

# Ubuntu Ollama inference
curl http://localhost:11435/api/chat -d '{
  "model": "qwen3.5:32b",
  "messages": [{"role": "user", "content": "Explain Ubuntu apt package management"}],
  "stream": false
}'

curl (OpenAI format)

curl http://localhost:11435/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "phi4", "messages": [{"role": "user", "content": "Hello from Ubuntu Ollama"}]}'

Ubuntu Ollama NVIDIA CUDA setup

# Install NVIDIA drivers on Ubuntu for Ollama CUDA
sudo apt install nvidia-driver-550 -y
sudo reboot

# Verify Ubuntu NVIDIA CUDA
nvidia-smi

# Ubuntu Ollama automatically uses CUDA when NVIDIA drivers are installed
ollama ps    # should show GPU acceleration

Ubuntu Ollama environment

# Optimize Ollama on Ubuntu via systemd
sudo systemctl edit ollama
# Add under [Service]:
#   Environment="OLLAMA_KEEP_ALIVE=-1"
#   Environment="OLLAMA_MAX_LOADED_MODELS=-1"
#   Environment="OLLAMA_NUM_PARALLEL=2"
sudo systemctl restart ollama

# Verify Ubuntu Ollama settings
systemctl show ollama | grep Environment

Ubuntu Ollama model recommendations

Ubuntu Machine	GPU	Best Ubuntu Ollama models
Ubuntu desktop (RTX 4090)	24GB	llama3.3:70b, qwen3.5:32b, deepseek-r1:32b
Ubuntu desktop (RTX 4080)	16GB	phi4, codestral, qwen3.5:14b
Ubuntu Server (A100)	80GB	deepseek-v3, qwen3.5:72b
Ubuntu Server (no GPU)	CPU	phi4-mini, gemma3:4b
Ubuntu on Raspberry Pi 5	CPU	gemma3:1b, phi4-mini

Ubuntu Ollama firewall

# Ubuntu UFW
sudo ufw allow 11435/tcp
sudo ufw reload

Monitor Ubuntu Ollama

# Ubuntu Ollama fleet status
curl -s http://localhost:11435/fleet/status | python3 -m json.tool

# Ubuntu Ollama health — 16 automated checks
curl -s http://localhost:11435/dashboard/api/health | python3 -m json.tool

# Ubuntu Ollama models loaded
curl -s http://localhost:11435/api/ps | python3 -m json.tool

# Ubuntu Ollama logs
journalctl -u herd-router -f
tail -f ~/.fleet-manager/logs/herd.jsonl.$(date +%Y-%m-%d)

Dashboard at http://localhost:11435/dashboard — live Ubuntu Ollama monitoring.

Also available on Ubuntu Ollama

Image generation

curl http://localhost:11435/api/generate-image \
  -d '{"model": "z-image-turbo", "prompt": "Ubuntu penguin in space", "width": 1024, "height": 1024}'

Embeddings

curl http://localhost:11435/api/embed \
  -d '{"model": "nomic-embed-text", "input": "Ubuntu Ollama local inference routing"}'

Full documentation

• Agent Setup Guide
• API Reference

Contribute

Ollama Herd is open source (MIT). Ubuntu Ollama users welcome:

• Star on GitHub
• Open an issue

Guardrails

• Ubuntu Ollama model downloads require explicit user confirmation.
• Ubuntu Ollama model deletion requires explicit user confirmation.
• Never delete or modify files in ~/.fleet-manager/.
• No models are downloaded automatically — all pulls are user-initiated or require opt-in.