Glimmer 30B: Prime Agent vs Hermes on RTX 3090

# Meta Muse Glimmer 30B Locally — Prime Agent vs Hermes

![Model](https://img.shields.io/badge/Model-Muse%20Glimmer%2030B-7c3aed?style=for-the-badge)
![GPU](https://img.shields.io/badge/Tested%20on-RTX%203090-76b900?style=for-the-badge&logo=nvidia&logoColor=white)
![Runtime](https://img.shields.io/badge/Runtime-llama.cpp-2563eb?style=for-the-badge)
![Harnesses](https://img.shields.io/badge/Harnesses-Prime%20Agent%20%7C%20Hermes-f59e0b?style=for-the-badge)

 Run **Muse Glimmer 30B** locally through an OpenAI-compatible endpoint, connect it to **Prime Agent** and **Hermes**, and compare both harnesses under the same conditions.

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## 📺 Watch the Video First

[![Watch on YouTube](https://img.shields.io/badge/▶_Watch_the_Full_Video-FF0000?style=for-the-badge&logo=youtube&logoColor=white)](https://www.youtube.com/watch?v=Lkdww0s_xxs)

### **Meta Muse Glimmer 30B Locally: Prime Agent vs Hermes on RTX 3090**

The video contains the complete walkthrough, performance measurements, architecture explanation, controlled test, workflow differences and final verdict.

 **This repository is intentionally a companion—not a replacement for the video.** It provides the essential commands and configuration references, while the complete benchmark prompt, test project and detailed results remain in the video.

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## What Is Covered?

- Running Muse Glimmer 30B locally on an RTX 3090
- VRAM usage, generation speed and context support
- Prime Agent's persistent IPython and RLM workflow
- Connecting Prime Agent to a local OpenAI-compatible endpoint
- Connecting Hermes to the same model
- Comparing both harnesses with the same project and instructions

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## Test Architecture

```text
                         ┌── Prime Agent
RTX 3090 → Muse Glimmer ─┤
      llama.cpp :8080    └── Hermes
```

Both harnesses use the same:

- Muse Glimmer model
- Local endpoint
- Context window
- Project copy
- User instructions

Only the **agent harness** changes.

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## Requirements

| Component | Tested configuration |
|---|---|
| Operating system | Linux / WSL2 |
| GPU | NVIDIA RTX 3090, 24 GB VRAM |
| Runtime | llama.cpp / llama.app |
| API endpoint | `http://127.0.0.1:8080/v1` |
| Context used | `65,536` tokens |
| Harnesses | Prime Agent and Hermes Agent |

Different quantizations and context sizes can change memory usage and speed.

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## 1. Start Muse Glimmer

Install or build [ll