Muse Glimmer 30B on a Qualcomm Dragonwing board

# Muse Glimmer 30B on Dragonwing IQ-9075: An LLM That Sees

Run Meta's 30B vision-language model entirely on a Dragonwing IQ-9075. Zero-shot defect inspection, tool calling and 128K context, no cloud.

**Author:** Samuel Alexander  
**Target:** Qualcomm Dragonwing IQ-9075 EVK  
**Model:** Meta Muse-Glimmer-30B (Apache-2.0), Meta's official GGUF build  
**Runtime:** llama.cpp, CPU backend  
**Measured:** 21.6 GB resident for a 30B model with its full 131K context, 8.13 tokens/s prefill, 2.84 tokens/s generation  
**Difficulty:** Intermediate  
**Time:** About an hour, most of it downloading 16.8 GB  
**Repository:** https://github.com/SamuelAlexander/dragonwing-muse-glimmer-30b

![A circuit board with four bent header pins, the 512 pixel image sent to the model, and the JSON verdict identifying bent pins in the lower-left header](images/hero.jpg)

I gave the board a photo of a PCB I had built badly on purpose and asked it, in plain English, to look for manufacturing defects. This is zero-shot: the model has never seen this board, this defect, or any inspection dataset of mine. It works from what it already knows. It looked the board over and came back with this:

```json
{"pass": false,
 "defect": "bent/misaligned header pins",
 "location": "lower left side of the board, first four pins of the bottom yellow header",
 "severity": "medium",
 "reason": "pins are visibly bent outward and not aligned with the header housing, indicating improper insertion or handling"}
```

That is correct. The lower-left header does have four splayed pins. Nothing left the board to work it out.

## Where this model comes from

Open-weight models have spent the last couple of years getting smaller and better at once. The releases that matter stopped being the ones that broke a benchmark and started being the ones you could put somewhere: a laptop, a workstation, a single GPU. The question changed with them. It used to be whether a model could write well. Now it is whether a model can *do* things: read a screenshot, pick the right function to call, notice its own last step failed and try again.

Muse Glimmer is Meta's entry in that direction, released in August 2026 under Apache-2.0. Roughly 30 billion parameters, about 1.8 billion of them a vision encoder at the front, distilled from Meta's larger Muse Spark. Meta positions it against Gemma4-31B and Qwen3.6-27B, so it is competing