Summary
Fahd Mirza demonstrates a complete local setup for running Hermes Agent using LM Studio as the model server, using an Ubuntu machine with an NVIDIA RTX 6000 (48GB VRAM) as the test environment. The tutorial starts from scratch — downloading LM Studio, loading the Qwen 3.5 9B model (6.5GB quantized), serving it on a local OpenAI-compatible endpoint at port 1234, and connecting Hermes Agent to that endpoint via its one-liner installer.
A key technical detail covered is the endpoint configuration: LM Studio’s server URL does not include a /v1 suffix by default, but the Hermes installer requires a fully qualified OpenAI-compatible base URL, so appending /v1 manually is necessary for the connection to work. The video also walks through the interactive configuration questions Hermes presents on first install — including whether to migrate from OpenClaw, which messaging integrations to enable, and optional vision or TTS features — showing which defaults are safe to skip.
Hermes Agent is framed throughout as a self-improving autonomous agent built by Nous Research: it creates reusable skills from tasks it completes, refines them over time, and builds a persistent memory model of the user across sessions. For developers wanting to run a capable local agent without cloud API costs, this video provides a clear path using Qwen 3.5 9B as the underlying model, with LM Studio handling model serving and Hermes managing the agent loop.
📺 Source: Fahd Mirza · Published March 23, 2026
🏷️ Format: Tutorial Demo
