The article discusses the increasing usefulness of running AI models locally, highlighting benefits like latency, privacy, cost, and control. It explores practical applications such as data processing, note-taking, voice assistance, and self-sufficiency, while acknowledging the limitations compared to cloud-based models.
A user shares their optimal settings for running the gpt-oss-120b model on a system with dual RTX 3090 GPUs and 128GB of RAM, aiming for a balance between performance and quality.
This article details how the author uses a local LLM to summarize Docker logs and other home lab logs, providing proactive insights into their self-hosted setup and improving maintenance.
This article details 7 lessons the author learned while self-hosting Large Language Models (LLMs), covering topics like the importance of memory bandwidth, quantization, electricity costs, hardware choices beyond Nvidia, prompt engineering, Mixture of Experts models, and starting with simpler tools like LM Studio.
LM Studio has released lms, a command-line interface (CLI) tool to load/unload models, start/stop the API server, and inspect raw LLM input. It is developed on GitHub and is MIT Licensed.
This is a GitHub repository for a Discord bot named discord-llm-chatbot. This bot allows you to chat with Large Language Models (LLMs) directly in your Discord server. It supports various LLMs, including those from OpenAI API, Mistral API, Anthropic API, and local models like ollama, oobabooga, Jan, LM Studio, etc. The bot offers a reply-based chat system, customizable system prompt, and seamless threading of conversations. It also supports image and text file attachments, and streamed responses.
