I switched from LM Studio/Ollama to llama.cpp, and I absolutely love it
If you're just getting started with running local LLMs, it's likely that you've been eyeing or have opted for LM Studio and Ollama. These GUI-based tools are the defaults for a reason. They make hosting and connecting to local AI models extremely easy, and it's how I supercharged my Raycast experience with AI. However, recently, I've made the decision to move to llama.cpp for my local AI setup. Yes, LM studio and Ollama offered everything I needed, including a polished interface and one-click model loading. But those conveniences come with trade-offs. From extra layers of abstraction to slower startup times, and less control over how the models actually run. Switching to llama.cpp strips all that away and gives you direct access, efficiency, and flexibility. It's now become my go-to recommendation for anyone with more than a fleeting interest in gaining more control over their local AI models, or interest in learning how they work.
If you’re just getting started with running local LLMs, it’s likely that you’ve been eyeing or have opted for LM Studio and Ollama. These GUI-based tools are the defaults for a reason. They make hosting and connecting to local AI models extremely easy, and it’s how I supercharged my Raycast experience with AI. However, recently, I’ve made the decision to move to llama.cpp for my local AI setup. Yes, LM studio and Ollama offered everything I needed, including a polished interface and one-click model loading. But those conveniences come with trade-offs. From extra layers of abstraction to slower startup times, and less control over how the models actually run. Switching to llama.cpp strips all that away and gives you direct access, efficiency, and flexibility. It’s now become my go-to recommendation for anyone with more than a fleeting interest in gaining more control over their local AI models, or interest in learning how they work.
Ahmet Limoncuoğlu
Turkey
Turkey
Published by: aplhsindia.in
