How I run LLMs locally

The article provides a guide on running Large Language Models (LLMs) locally, crediting the contributions of artists, coders, and writers whose work underpins these models. It suggests starting with resources like the r/LocalLLaMA subreddit and the Ollama blog. The author describes their hardware setup, which includes a Linux laptop with a core i9 CPU, 4090 GPU, and 96 GB of RAM, noting that while powerful hardware can improve performance, smaller models can run on older systems. They use various tools such as Ollama, Open WebUI, llamafile, and others for running LLMs, image generation, and code completion. The author also mentions using Smart Connections in Obsidian for querying notes and highlights the importance of open-source tools and models.

The author discusses their approach to selecting and updating models, using resources like the Ollama models page and CivitAI for image generation models. They mention using WatchTower for updating docker containers and express caution about fine-tuning or quantizing models due to potential hardware issues. The article concludes by emphasizing the benefits of running LLMs locally, such as data control and reduced latency, and acknowledges the foundational work of open-source projects and data owners. The author invites readers to subscribe to their newsletter for content on various topics.

Key takeaways:

Running LLMs locally provides control over data and reduces response latency, but requires specific hardware and software tools.
Ollama, Open WebUI, and llamafile are key tools for managing and running LLMs locally, with each serving different functions.
Model selection is based on performance and size, with frequent updates due to rapid advancements in LLM technology.
Fine-tuning and quantization are not performed due to potential hardware limitations, but updates are managed using WatchTower and Open Web UI.

How I run LLMs locally

Key takeaways:

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