Unsloth Muse Glimmer 30B Guide 2026: Run & Fine-Tune Meta's Agent Model on 18GB RAM

"I want to run Meta's Muse Glimmer on my own PC, but how?"

"I want to know how to pick GGUFs, recommended settings, and fine-tuning..."

The answer is here. Unsloth, the go-to tool for local LLM operations, has addedday-zero supportfor Meta's new model,Muse Glimmer 30B.

With Unsloth's Dynamic quantization, you can run Muse Glimmer on 18GB RAM. Sizes range from 2-bit to 8-bit, so you can pick the best balance for your PC.Fine-tuning is also supported.

In this article, I'll fully explain how to run Muse Glimmer with Unsloth — in beginner-friendly terms.

What You'll Learn in This Article

Overview of Unsloth's Muse Glimmer support (runs on 18GB RAM)
Hardware requirements (quick-reference table for 2-bit to 8-bit)
Recommended settings (temperature, top_p, top_k, context length)
How to actually run it (Unsloth Desktop, llama.cpp)
Fine-tuning and benchmarks

What Is Unsloth's Muse Glimmer Support?

Unsloth is an open-source tool specialized in running, accelerating, and fine-tuning local LLMs.

On August 10, 2026, Unsloth announced day-zero supportfor Meta'sMuse Glimmer 30B (Apache 2.0, multimodal, agent-focused model).

Key points

Runs on 18GB RAM/VRAM: works on Mac, GPU, and CPU systems alike
Unsloth Dynamic quantization: maximizes accuracy while fitting smaller devices like the RTX 5090
Both run and fine-tune: everything is done in Unsloth Desktop or the Python library
Co-developed llama.cpp implementation with Meta and Hugging Face

On X, it's gathering 151,000+ views and 1,600+ likes.

Hardware Requirements: Quick Reference (2-bit to 8-bit)

Muse Glimmer 30B needs about 58GB at full precision (BF16), but Unsloth Dynamic quantization dramatically reduces that.

Quantization	Model size	Recommended RAM	Hardware examples
2-bit (UD-Q2_K_XL)	12–15GB	18–20GB+	RTX 4080
3-bit (UD-Q3_K_XL)	12–15GB	20–24GB+	RTX 4090
4-bit (UD-Q4_K_XL, NVFP4)	17–19GB	24–32GB+	Mac 32GB
6-bit (UD-Q6_K_XL)	24–26GB	32–40GB+	RTX 5090, Mac 48GB
8-bit (UD-Q8_K_XL)	31–33GB	40–48GB+	Mac 128GB, DGX Spark
BF16 (full precision)	55.7GB	58GB+	Mac 128GB, DGX Spark

Rule of thumb: as long as your total available memory exceeds the size of the quantized model, it runs comfortably. If not, llama.cpp can still run with partial RAM/disk offload, but generation will be slower.

Recommended Settings (Meta's Official Defaults)

Parameter	Recommended value	Notes
temperature	1.0	Meta default
top_p	0.95	Maintains diversity
top_k	64	Narrows choices while staying flexible
Context length	131,072 (up to 262,144)	~131K tokens by default
Reasoning strength	low / medium / high / xhigh	Controllable Effort

Method 1: Unsloth Desktop (Easiest)

Step 1: Install Unsloth

Download: get the app from unsloth.ai/download
macOS, Windows, Linux supported

You can also install via command:

# macOS / Linux / WSL
curl -fsSL https://unsloth.ai/install.sh | sh

# Windows PowerShell
irm https://unsloth.ai/install.ps1 | iex

Step 2: Search & download Muse Glimmer

After first launch, go to the Model hub tab, search for "Muse Glimmer", and download your preferred quantization.

Step 3: Run

Inference parameters are auto-set, but you can change them manually. Context length, chat templates, and GGUF/MLX files are all supported.

Method 2: llama.cpp (CLI)

Here's an example using Unsloth's Dynamic 4-bit (UD-Q4_K_XL).

Build

apt-get update
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
git clone https://github.com/ggml-org/llama.cpp
cmake llama.cpp -B llama.cpp/build \
    -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON
cmake --build llama.cpp/build --config Release -j --clean-first --target llama-cli llama-mtmd-cli llama-server llama-gguf-split
cp llama.cpp/build/bin/llama-* llama.cpp

Run

export LLAMA_CACHE="unsloth/Muse-Glimmer-30B-GGUF"
./llama.cpp/llama-cli \
    -hf unsloth/Muse-Glimmer-30B-GGUF:UD-Q4_K_XL \
    --temp 1.0 \
    --top-p 0.95 \
    --top-k 64

Fine-Tuning Is Also Possible

With Unsloth, you can fine-tune Muse Glimmer 30B too.

2x faster training with 70% less VRAM
Prepare your own training data for your use case
See the Fine-tune Muse Glimmer docs for details

Benchmarks: The Strongest Agent Model in Its Size Class

In the benchmarks Unsloth published, Muse Glimmer-30B High Reasoningbeats same-sizeGemma4-31BandQwen3.6-27B in many categories.

Category	Benchmark	Muse Glimmer 30B	Gemma4-31B	Qwen3.6-27B
Agentic	MCP Atlas (Public)	75.5	54.2	62.5
Agentic	DeepSearch QA	74.6	61.7	71.1
Agentic	Gaia2	43.3	36.4	40.0
Coding	SWE-Bench Pro	51.2	36.9	50.2
Coding	SWE-Bench Verified	76.0	66.6	77.2
Coding	SciCode	43.6	43.4	39.8
Multimodal	Charxiv Reasoning	78.8	77.7	78.4
Multimodal	OmniDocBench v1.5	75.8	72.5	77.8
Reasoning	AIME 2026	94.7	89.2	94.1
Reasoning	IFBench	77.0	76.0	70.8

(Bold indicates the best score. Muse Glimmer leads in most categories.)

Summary

Unsloth's Muse Glimmer support is the fastest route to starting local AI agents.

Runs on 18GB RAM: choose from 2-bit to 8-bit for your environment
Day-zero support: usable in Unsloth from Meta's first day of release
Run and train: inference and fine-tuning both supported
Strongest in its class: leads Gemma4 and Qwen3.6 in most benchmarks

If you want to run Muse Glimmer on your own PC, install Unsloth Desktop and search for Muse Glimmer in the Model hub. Your local AI agent will be running in minutes.