Ollama clustering scales local LLMs

Ollama is outgrowing its single-node roots as developers demand production-grade, distributed local inference capabilities. Community projects like olol now provide the model-aware routing and load balancing missing from the official binary, while distributed file systems like JuiceFS are becoming essential for managing multi-gigabyte model weights without storage duplication. Although high-latency networking remains a primary hurdle for distributing weights and context across disparate cluster nodes, the push for horizontal scaling highlights a shift from individual experimentation to multi-user enterprise AI deployments necessary to compete with cloud-managed providers.