№ 0051GitHub★ Pick
Muse Glimmer 30B on a Qualcomm Dragonwing board
Samuel Alexander ran Muse Glimmer 30B entirely on a Qualcomm Dragonwing IQ-9075 board for zero-shot PCB defect inspection and tool calling, measuring 21.6 GB resident with full 131K context and 2.84 tokens/s generation.
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
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:
{"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
