Ollama — Herd Your LLMs Into One Smart Endpoint

Ollama fleet router — herd your Ollama LLMs into one smart endpoint. Route Llama, Qwen, DeepSeek, Phi, Mistral, and Gemma across multiple devices with 7-signal scoring, auto-retry, VRAM-aware fallback, and context protection. Plus image generation, speech-to-text, and embeddings. Drop-in OpenAI SDK compatible.

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

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)

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)

# 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:

Model	Sizes	Best for
llama3.3	8B, 70B	General purpose
qwen3	0.6B–235B	Multilingual, reasoning
qwen3.5	0.8B–397B	Latest generation
deepseek-v3	671B (37B active)	Matches GPT-4o
deepseek-r1	1.5B–671B	Reasoning (like o3)
phi4	14B	Small, fast, capable
mistral	7B	Fast, European languages
gemma3	1B–27B	Google's open model
codestral	22B	Code generation
qwen3-coder	30B (3.3B active)	Agentic coding
nomic-embed-text	137M	Embeddings 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

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

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

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

Embeddings

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:

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.