Ollama — Herd Your LLMs Into One Endpoint#

You have Ollama running on multiple machines. This skill gives you one endpoint that routes every request to the best available device automatically. No more hardcoding IPs, no more manual load balancing, no more "which machine has that model loaded?"

Setup#

bash

pip install ollama-herd
herd              # start the router on port 11435
herd-node         # run on each machine with Ollama

Now point everything at http://localhost:11435 instead of http://localhost:11434. Same Ollama API, same models, smarter routing.

Package: ollama-herd | Repo: github.com/geeks-accelerator/ollama-herd

Use your Ollama models through the fleet#

OpenAI SDK (drop-in)#

python

from openai import OpenAI

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": "Hello"}],
    stream=True,
)
for chunk in response:
    print(chunk.choices[0].delta.content or "", end="")

Ollama API (same as before, different port)#

bash

# Chat
curl http://localhost:11435/api/chat -d '{
  "model": "qwen3:235b",
  "messages": [{"role": "user", "content": "Hello"}],
  "stream": false
}'

# List all models across all machines
curl http://localhost:11435/api/tags

# Models currently in GPU memory
curl http://localhost:11435/api/ps

# Embeddings
curl http://localhost:11435/api/embeddings -d '{
  "model": "nomic-embed-text",
  "prompt": "search query"
}'

What the router does#

When a request comes in, the router scores every online node on 7 signals:

1. Thermal — is the model already loaded in GPU memory? (+50 for hot)
2. Memory fit — how much headroom does the node have?
3. Queue depth — how many requests are waiting?
4. Wait time — estimated latency based on history
5. Role affinity — large models prefer big machines
6. Availability — is the node reliably available?
7. Context fit — does the loaded context window fit the request?

The highest-scoring node handles the request. If it fails, the router retries on the next best node automatically.

Supported Ollama models#

Any model that runs on Ollama works through the fleet. Popular ones:

ModelSizesBest forllama3.38B, 70BGeneral purposeqwen30.6B–235BMultilingual, reasoningqwen3.50.8B–397BLatest generationdeepseek-v3671B (37B active)Matches GPT-4odeepseek-r11.5B–671BReasoning (like o3)phi414BSmall, fast, capablemistral7BFast, European languagesgemma31B–27BGoogle's open modelcodestral22BCode generationqwen3-coder30B (3.3B active)Agentic codingnomic-embed-text137MEmbeddings for RAG

Resilience features#

• Auto-retry — re-routes to next best node on failure (before first chunk)
• VRAM-aware fallback — routes to a loaded model in the same category instead of cold-loading
• Context protection — prevents num_ctx from triggering expensive model reloads
• Zombie reaper — cleans up stuck in-flight requests
• Auto-pull — downloads missing models to the best node automatically

Also available#

The same fleet router handles three more workloads:

Image generation#

bash

curl -o image.png http://localhost:11435/api/generate-image \
  -H "Content-Type: application/json" \
  -d '{"model":"z-image-turbo","prompt":"a sunset","width":1024,"height":1024,"steps":4}'

Enable: curl -X POST .../dashboard/api/settings -d '{"image_generation":true}'

Speech-to-text#

bash

curl http://localhost:11435/api/transcribe -F "audio=@recording.wav"

Enable: curl -X POST .../dashboard/api/settings -d '{"transcription":true}'

Embeddings#

bash

curl http://localhost:11435/api/embeddings -d '{"model":"nomic-embed-text","prompt":"text"}'

Already enabled — routes through Ollama automatically.

Dashboard#

http://localhost:11435/dashboard — 8 tabs: Fleet Overview, Trends, Model Insights, Apps, Benchmarks, Health, Recommendations, Settings. Real-time queue visibility with [TEXT], [IMAGE], [STT], [EMBED] badges.

Request tagging#

Track per-project usage:

python

response = client.chat.completions.create(
    model="llama3.3:70b",
    messages=messages,
    extra_body={"metadata": {"tags": ["my-project", "reasoning"]}},
)

Full documentation#

Agent Setup Guide

Guardrails#

• Never restart the router or node agents without user confirmation.
• Never delete or modify files in ~/.fleet-manager/.
• Never pull or delete models without user confirmation.