Muse Glimmer 30B — Q2_K

A low-memory Q2_K quantization of Meta’s Muse Glimmer 30B, built directly from the official BF16 weights.

Designed to make Muse Glimmer 30B practical on lower-memory hardware, including Apple Silicon Macs.

Quick Start

ollama run yogeshjog/muse-glimmer-q2

Or explicitly use the Q2_K tag:

ollama run yogeshjog/muse-glimmer-q2:q2_k

Model

Property	Value
Base model	`meta-models/Muse-Glimmer-30B`
Quantization	Q2_K
Format	GGUF
Quantized GGUF	9.95 GiB
Bits per weight	3.07 BPW
Source precision	BF16
Quantizer	llama.cpp

The model was quantized directly from the official BF16 weights.

It was not requantized from an existing Q4 or other quantized checkpoint.

Official Muse-Glimmer-30B BF16
              │
              ▼
          BF16 GGUF
              │
              ▼
       llama.cpp Q2_K
              │
              ▼
       9.95 GiB GGUF
              │
              ▼
            Ollama

Tested on Apple Silicon

Created and tested locally on:

MacBook Pro
Apple M3 Max
36 GB unified memory

Tested with Ollama at a configured 32K context window:

NAME                      SIZE     PROCESSOR    CONTEXT
muse-glimmer-q2:latest    11 GB    100% GPU     32768

The model successfully loaded with 100% GPU offload.

Actual memory consumption can vary depending on context utilization, KV cache configuration, Ollama version, and workload.

Why Q2_K?

Muse Glimmer 30B is relatively large for consumer hardware.

Q2_K reduces the model weights to approximately:

9.95 GiB
3.07 BPW

The objective is simple:

How much of Muse Glimmer 30B can we retain while bringing the model down to roughly 10 GiB?

This makes it particularly interesting for:

Apple Silicon
lower-memory GPUs
laptops and workstations
local inference
larger context experiments
memory-constrained environments

Quality Warning

Q2_K is an aggressive quantization.

Expect some quality degradation compared with BF16 and higher-bit quantizations such as Q4, Q5, Q6, or Q8.

Potentially affected capabilities include:

reasoning
coding
instruction following
factual accuracy
structured output
long-context performance
multimodal performance

Evaluate the model for your particular workload.

Quantization Details

Observed llama.cpp output:

model size = 53131.48 MiB (16.00 BPW)
quant size = 10179.12 MiB (3.07 BPW)

quantize time = 79786.76 ms

The entire conversion and quantization process was performed locally on an M3 Max with 36 GB unified memory.

No cloud GPU was required.

GGUF Download

The raw Q2_K GGUF and full model card are available on Hugging Face:

https://huggingface.co/yogeshjog/Muse-Glimmer-30B-Q2_K-GGUF

Reproduce the Quantization

The quantization was produced using:

https://github.com/phygineer/quantize

Pipeline:

Hugging Face BF16
      ↓
BF16 GGUF
      ↓
llama.cpp
      ↓
Q2_K

Original Model

Original model:

https://huggingface.co/meta-models/Muse-Glimmer-30B

Please refer to the original model repository for authoritative documentation, intended use, limitations, safety information, and licensing details.

Credits

Original model: Meta Superintelligence Labs
Base model: meta-models/Muse-Glimmer-30B
Quantization: Q2_K
GGUF / quantization tooling: llama.cpp
Quantization pipeline: phygineer/quantize
Quantized by: yogeshjog

Disclaimer

This is an unofficial community quantization and is not affiliated with or endorsed by Meta.

Low-bit quantization changes model weights and can materially affect model behavior and output quality.

Models

Readme