How to Run Hermes Agent v0.18.0 on Your RTX PC

Hermes Agent v0.18.0 — dubbed The Judgment Release — landed on July 1, 2026, and it's the most polished open-source agent framework you can run locally today. With 100% of P0/P1 issues resolved, 370+ community contributors, and a new partnership with NVIDIA that brings self-improving agents to RTX PCs and DGX Spark, now is the perfect time to set it up on your own hardware.

In this tutorial, we'll walk through installing Hermes Agent v0.18.0, configuring it with a local LLM via Ollama or LM Studio, and connecting it to the NVIDIA GPU acceleration that makes it fly. No cloud dependencies required.

What's New in v0.18.0 That Makes Local Deployments Better

Before we get into the commands, here's why this release matters for local users. The Judgment Release introduced Mixture-of-Agents (MoA) as a first-class feature — named ensembles of models you can pick like any single model, with every reference model's reasoning shown in-stream. The agent can now verify its own work against evidence rather than guessing, and the /learn and /journey commands let you see exactly how the agent improves over time.

Under the hood, the gateway supports scale-to-zero and drain coordination — designed for always-on local use. Sub-agents can now fan out in the background, and the desktop gained first-class coding projects with a playable memory graph.

Step 1: Hardware Requirements

You need an NVIDIA GPU to take full advantage. Here's what works:

RTX 4090 / 5090: Runs Qwen 3.6 27B with ease (~20GB VRAM)
RTX 6000 Ada / PRO: Handles Qwen 3.6 35B comfortably
DGX Spark: NVIDIA's always-on agentic computer — purpose-built for 24/7 Hermes operation
RTX 4070+: Great for 7B-14B parameter models and fast skill refinement

The key insight from the NVIDIA partnership announcement: better hardware means better agents. Hermes is an active orchestration layer that benefits enormously from low-latency local inference.

Step 2: Install Hermes Agent

Open a terminal and run:

git clone https://github.com/NousResearch/hermes-agent.git
cd hermes-agent
git checkout v2026.7.1  # The Judgment Release
pip install -e .
hermes setup

The setup wizard will walk you through creating a profile, selecting a provider, and configuring your first skill. For local inference, choose Ollama or LM Studio during setup.

Step 3: Set Up a Local LLM

The recommended model for RTX-class hardware is Qwen 3.6 27B — it matches the accuracy of 400B-parameter models at one-sixteenth the size. With Ollama:

ollama pull qwen3.6:27b
hermes config set model qwen3.6:27b
hermes config set provider ollama

For DGX Spark or high-end RTX GPUs, the Qwen 3.6 35B model is even better, surpassing 120B-parameter predecessors on agentic benchmarks. Just bump the config:

hermes config set model qwen3.6:35b

Step 4: Enable NVIDIA Acceleration

Hermes automatically detects CUDA when running with Ollama or LM Studio. Verify your GPU is active:

hermes doctor

This shows your inference backend, model name, and whether GPU layers are loaded. You should see CUDA: YES and a layer count in the hundreds. If not, ensure your NVIDIA drivers are current and restart Ollama with:

ollama serve

Step 5: Test Your First Task

Now for the fun part. Ask Hermes to do something practical:

hermes "Analyze the Python files in this project and suggest three optimizations"

With v0.18.0, you get completion contracts — the agent commits to a plan before executing, so you see exactly what it intends to do. If you want to watch it learn, use the /learn command after a successful task:

/learn

This saves the approach as a skill, and next time you ask something similar, Hermes already knows how to tackle it.

Tips for Always-On Deployments

Run Hermes as a daemon with hermes start --background — the gateway will scale to zero when idle
Pair with DGX Spark for a dedicated agentic appliance that never thermal-throttles
Use /goal with completion contracts for long-running tasks — the agent self-verifies and won't stop until evidencable checkpoints are met
Enable the desktop UI with hermes gui for a visual memory graph and coding project explorer

Why You Should Upgrade Now

The Judgment Release isn't just a clean-sweep bugfix — it's a declaration of stability. With all P0/P1 issues closed, 1,720 commits of polish, and NVIDIA's backing for local-first agentic AI, Hermes v0.18.0 is ready for production use on your own hardware. Whether you're building a coding assistant, a research pipeline, or a 24/7 smart-home controller, this is the release that makes local agents genuinely reliable.

Get started today: clone the repo, run the setup, and let Hermes start improving itself on your RTX hardware tonight.