This work investigates the time, token, and energy overhead of locally deployed LLM-based AI agents on consumer hardware. It reveals that while local agents address privacy and cost concerns, their iterative reasoning and tool use substantially increase resource consumption, leading to higher GPU power draw and battery drain.
This course covers the coding essentials for deploying and running Large Language Models (LLMs) locally and in cloud environments. It provides practical knowledge on managing these models in various operational setups.
This is a practical guide for deploying local LLMs, detailing hardware, operating system, and prerequisite installation requirements. It compares frameworks like llama.cpp, Ollama, and vLLM for different development and performance needs.
Google's Gemma 4, released in 2026 under an Apache 2.0 license, is hailed as the most developer-friendly open model. It runs locally and can replace API calls, though it has some JSON tool-call formatting bugs when used agentically.
This guide provides practical steps for setting up Large Language Models (LLMs) locally on a Linux system, detailing hardware requirements and performance benchmarks. It compares frameworks like llama.cpp, Ollama, vLLM, and LocalAI, recommending llama.cpp with setup instructions for model deployment.
This beginner-friendly guide shows how to securely run the AI tool OpenClaw locally on Windows using Windows Sandbox, avoiding complex cloud infrastructure. It details common security risks, isolation options, and provides steps for setting up and troubleshooting the sandbox.