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How To Host AI Locally
NBTV: Your Money, Your Data, Your Life
Local AI Stack
Summary: Running Large Language Models (LLMs) locally involves a stack of open-source tools. The base layer is the LLM itself, like LLaMA. An AI engine, such as llama.cpp, allows interaction with the model. Finally, an interface like Ollama simplifies the user experience. Insights:
- Various open-source LLMs exist, trained on different datasets. LLaMA is a prominent example, developed by Meta.
- AI engines are needed to interact with LLMs. Llama.cpp is an open-source engine designed for local use.
- Interfaces like Ollama enhance the user experience by automating setup and management. Proper Nouns:
- LLaMA: An open-source LLM developed by Meta.
- llama.cpp: An open-source AI engine for interacting with LLMs locally.
- Ollama: An interface that simplifies local LLM setup and interaction.
- Meta: The company that developed LLaMA.
- Ryan Condren: Works on decentralized AI compute. Research
- What are the advantages and disadvantages of running LLMs locally compared to cloud-based solutions?
- How does the size of an LLM (e.g., number of parameters) affect its performance and resource requirements?
- What are other examples of AI engines and interfaces for local LLM interaction, besides llama.cpp and Ollama?
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