Running large language models locally often runs into hardware limitations that prevent complex problem-solving. This article explains a hybrid approach where a local model acts as a junior engineer for routine tasks but escalates difficult issues to cloud-based models like Claude when it gets stuck. This orchestration system allows for a privacy-focused, local-first workflow without sacrificing the high-level reasoning power of massive commercial AI.
- Ollama for local inference and model management
- LiteLLM as a routing layer to provide a unified API for both local and cloud models
- OpenRouter or Anthropic's API for flexible cloud escalation
- A simple orchestration system to manage retries and task handovers
- Ollama for local inference and model management
- LiteLLM as a routing layer to provide a unified API for both local and cloud models
- OpenRouter or Anthropic's API for flexible cloud escalation
- A simple orchestration system to manage retries and task handovers
