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Glimmer 30B: Prime Agent vs Hermes on RTX 3090
A video companion repo with the commands and configs for running Muse Glimmer 30B locally on an RTX 3090 and comparing the Prime Agent and Hermes harnesses under the same conditions.
Meta Muse Glimmer 30B Locally — Prime Agent vs Hermes
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.
📺 Watch the Video First
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.
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
Test Architecture
┌── 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.
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.
1. Start Muse Glimmer
Install or build [ll
