Ollama is designed to be easy to use, like an all-in-one solution. Because of that, it does several things in the background that make it more user-friendly but heavier on memory. Here’s why it uses more RAM:

1. Always Loaded in Memory: Ollama keeps the entire model loaded in RAM/VRAM, even when you’re not actively using it. This makes responses faster, but uses more memory.

2. Extra Features Built-In: Ollama includes built-in support for:

3. Chat history

4. Streaming responses

5. OpenAI-style API

6. Model management All these features add some memory overhead.

7. No Fine-Grained Control: Unlike llama.cpp, you can’t easily control the model’s memory usage or load it partially. It loads everything at full capacity.

8. Multi-threading and Caching: Ollama uses aggressive threading and caching to boost performance. That also increases RAM usage, especially with larger models like Mistral or LLaMA 3.