Ollama has become a popular tool for running large language models locally, but understanding its internal architecture is key to optimizing performance in production. This analysis breaks down the inference pipeline, starting with model loading and memory management, then moving to the critical continuous batching mechanism that enables high throughput. The article also covers practical tuning parameters such as batch size, context length, and GPU memory allocation. For engineers deploying Ollama in production, these insights help reduce latency and improve resource utilization. The content is evergreen and commercially valuable for AI infrastructure teams looking to scale local LLM serving.
A deep dive into Ollama's architecture, covering model loading, KV cache, and continuous batching for production tuning.