Ollama Ollama Herdv1.2.1

A research team runs multiple Ollama instances on heterogeneous hardware (macOS laptops, Linux servers, Windows desktops). Ollama Herd automatically routes each request to the best available node based on GPU memory, queue depth, and latency, allowing efficient utilization of all devices without manual management.

A startup with limited budget uses Ollama Herd to pool together several older machines into a single AI endpoint. The router's VRAM-aware fallback and auto-retry ensure reliable service for chatbots and document analysis without paying for cloud GPUs.

A legal firm deploys Ollama Herd across secure on-premise servers to process confidential documents using local LLMs for summarization and extraction. The embedding endpoint enables RAG, while the dashboard provides usage visibility and audit trails.

A global e-commerce company uses Ollama Herd to route customer queries to the best available node running multilingual models like Qwen3. Auto-pull ensures models are available on nodes with capacity, and the queue management prevents overload during peak hours.

A media company leverages Ollama Herd's STT endpoint to transcribe audio recordings from multiple journalists. The fleet routes requests to nodes with audio processing capabilities, and auto-retry handles transient failures, ensuring high throughput.

Offer a free tier with limited node count (e.g., 2 nodes) and basic routing. Charge monthly subscription for unlimited nodes, advanced dashboard features, priority support, and custom model auto-pulling policies.

Provide fully managed Ollama Herd clusters for clients who want AI inference without hardware management. Deploy, monitor, and maintain the fleet; bill based on number of nodes and query volume.

License Ollama Herd to hardware manufacturers (e.g., edge AI devices, local servers) as a value-add software layer. Optimized for their hardware, enabling turnkey local AI solutions.