windows-ollama

name	windows-ollama
description	Windows Ollama — run Ollama on Windows with fleet routing across multiple Windows PCs. Windows Ollama setup for Llama, Qwen, DeepSeek, Phi, Mistral. Route Ollama inference across Windows machines with NVIDIA RTX GPUs. Windows Ollama load balancing, health monitoring, and real-time dashboard. Windows Ollama本地推理。Windows Ollama enrutador IA.
version	1.0.0
homepage	https://github.com/geeks-accelerator/ollama-herd
metadata	{"openclaw":{"emoji":"windows","requires":{"anyBins":["curl","wget"],"optionalBins":["python3","pip","nvidia-smi"]},"configPaths":["~/.fleet-manager/latency.db","~/.fleet-manager/logs/herd.jsonl"],"os":["windows"]}}

Windows Ollama — Fleet Routing for Ollama on Windows

Run Ollama on Windows with multi-machine load balancing. Windows Ollama Herd turns multiple Windows PCs running Ollama into one smart endpoint. Your gaming desktop, your work laptop, your old tower — all serving AI requests through one Windows Ollama URL.

Windows Ollama setup

Step 1: Install Ollama on Windows

Download Ollama from ollama.ai and install. Ollama on Windows runs natively with NVIDIA GPU support.

Step 2: Install Windows Ollama Herd

pip install ollama-herd

Step 3: Start the Windows Ollama router

On one Windows PC (your router):

herd          # starts Windows Ollama router on port 11435
herd-node     # registers this Windows PC

On every other Windows PC:

herd-node     # auto-discovers the Windows Ollama router

mDNS issues on Windows? Corporate networks often block mDNS. Use explicit connection: herd-node --router-url http://router-ip:11435

Step 4: Verify Windows Ollama fleet

curl http://localhost:11435/fleet/status

You should see all your Windows Ollama nodes listed.

Use Windows Ollama

OpenAI SDK (Python)

from openai import OpenAI

# Your Windows 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 a PowerShell script to monitor GPU usage"}],
    stream=True,
)
for chunk in response:
    print(chunk.choices[0].delta.content or "", end="")

curl (Ollama format)

curl http://localhost:11435/api/chat -d '{
  "model": "qwen3.5:32b",
  "messages": [{"role": "user", "content": "Explain Windows GPU drivers"}],
  "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 Windows"}]}'

Windows Ollama environment setup

Keep models loaded in GPU memory on Windows:

# Windows environment variables (PowerShell)
[System.Environment]::SetEnvironmentVariable("OLLAMA_KEEP_ALIVE", "-1", "User")
[System.Environment]::SetEnvironmentVariable("OLLAMA_MAX_LOADED_MODELS", "-1", "User")
[System.Environment]::SetEnvironmentVariable("OLLAMA_NUM_PARALLEL", "2", "User")
# Restart Ollama from the Windows system tray

Verify Windows Ollama settings:

[System.Environment]::GetEnvironmentVariable("OLLAMA_KEEP_ALIVE", "User")

Windows Ollama model recommendations

Windows PC	GPU	Best Windows Ollama models
Gaming desktop (RTX 4090)	24GB vRAM	llama3.3:70b, qwen3.5:32b, deepseek-r1:32b
Gaming desktop (RTX 4080)	16GB vRAM	qwen3.5:14b, phi4, codestral
Work laptop (RTX 4060)	8GB vRAM	phi4-mini, gemma3:4b, llama3.2:3b
Office desktop (no GPU)	CPU only	phi4-mini, gemma3:1b — slower but works

Windows Ollama works with or without an NVIDIA GPU. CPU inference is slower but functional.

Windows Ollama troubleshooting

Windows Firewall

Allow Ollama Herd through Windows Firewall:

netsh advfirewall firewall add rule name="Ollama Herd" dir=in action=allow protocol=tcp localport=11435

Check Windows Ollama is listening

netstat -ano | findstr :11435    # Windows Ollama router
netstat -ano | findstr :11434    # Ollama itself

Windows Ollama logs

# Check Windows Ollama fleet health
curl http://localhost:11435/dashboard/api/health | python3 -m json.tool

Monitor Windows Ollama

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

# Models on Windows Ollama nodes
curl -s http://localhost:11435/api/ps | python3 -m json.tool

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

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

Also available on Windows Ollama

Image generation (Ollama native — works on Windows)

curl http://localhost:11435/api/generate-image `
  -d '{"model": "z-image-turbo", "prompt": "Windows desktop wallpaper", "width": 1024, "height": 1024}'

Embeddings

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

Full documentation

• Agent Setup Guide
• API Reference

Contribute

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

• Star on GitHub
• Open an issue

Guardrails

• Windows Ollama model downloads require explicit user confirmation.
• Windows 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.