Muse Glimmer Local Test | Meta's New Open Model | Coding with OpenCode, Agentic Work, Frontend

Name: Muse Glimmer Local Test | Meta's New Open Model | Coding with OpenCode, Agentic Work, Frontend
Uploaded: 2026-08-10T20:03:34.995Z
Duration: 11 min 7 s
Channel: Venelin Valkov
Description: Practical test of Muse Glimmer, a 30B multimodal open-source model by Meta, loaded via llama.cpp server. The video explores the performance and capabilities of this agentic model compared to other open-weight models.

Venelin Valkov10 August 2026Watch on YouTube

Part of series

Ep. 6 · Cpp Llama Local

View the series

Description

Muse Glimmer is 30B multimodal (Dense) open weight model by Meta AI. Benchmarks suggest better numbers than Qwen3.6 and Gemma 4. We'll load it using llama.cpp server locally and test to see how good it is! Announcement: https://research.meta.ai/blog/introducing-muse-glimmer-open-agentic-model Weights: https://huggingface.co/unsloth/Muse-Glimmer-30B-GGUF AI Academy: https://MLExpert.io Work with me: https://mlexpert.io/consulting DaBench (open weight LLM benchmarks): https://dabench.ai LinkedIn: https://www.linkedin.com/in/venelin-valkov Follow me on X: https://twitter.com/venelin_valkov Discord: https://discord.gg/UaNPxVD6tv Subscribe: http://bit.ly/venelin-subscribe GitHub repository: https://github.com/curiousily/AI-Bootcamp 👍 Don't Forget to Like, Comment, and Subscribe for More Tutorials! Join this channel to get access to the perks and support my work: https://www.youtube.com/channel/UCoW_WzQNJVAjxo4osNAxd_g/join

What you'll learn

Muse Glimmer is a 30B multimodal open-weight model from Meta AI that can be loaded locally via llama.cpp server
The model demonstrates better benchmark results compared to similar open-weight models like Qwen 3.6 and Gemma 4
Learn how to practically test and evaluate agentic models on local hardware

Frequently asked questions

What is Muse Glimmer and what makes it special?

Muse Glimmer is a 30B multimodal open-weight model from Meta AI. It stands out by achieving better benchmark results than comparable models like Qwen 3.6 and Gemma 4, while being available as open-source for local deployment.

How can you run Muse Glimmer locally?

You can load and run Muse Glimmer locally using llama.cpp server. The model weights are available via Hugging Face, enabling you to run it on your own hardware without relying on cloud services.

What are the benefits of testing agentic models locally?

Local testing gives you full control, privacy benefits, and independence from external APIs. You can directly assess performance on your own hardware and adapt the model to your specific needs.

Topics

open-source models multimodal ai model benchmarking agentic systems local inference