text model · Muse · macOS
Can I run Muse Glimmer 30B on Apple M5 Pro (48GB)?
Yes. Muse Glimmer 30B runs on Apple M5 Pro (48GB) at Q4_K_M (~18.2 GB of ~32 GB usable).
Runs at Q4_K_M using ~18.2 GB of ~32 GB usable.
That figure is at a 4k context and moves about ±15% as context length changes. Apple M5 Pro (48GB) leaves ~13.8 GB of headroom.
- Q4_K_M needed
- ~18.2 GB
- Usable on device
- ~32 GB
- Device memory
- 48 GB
- Best quant
- Q4_K_M
Which quant fits
Run it
Pick your tool. All 3 load the same Q4_K_M weights.
ollama run muse-glimmer:30b llama-cli -hf bartowski/Muse-Glimmer-30B-GGUF:Q4_K_M lms get bartowski/Muse-Glimmer-30B-GGUF - Parameters
- 30B
- Q4_K_M size
- 16.12 GB
- Q8_0 size
- 30.12 GB
- Context
- 128k
- Ollama tag
- muse-glimmer:30b
- Memory
- 48 GB unified
- Usable for weights
- ~32 GB
- Best runtime
- MLX direct / Ollama (MLX backend)
You could also run
Run Muse Glimmer 30B on other hardware
FAQ
Can Apple M5 Pro (48GB) run Muse Glimmer 30B?
Yes. Muse Glimmer 30B runs on Apple M5 Pro (48GB) at Q4_K_M (~18.2 GB of ~32 GB usable).
How much memory does Muse Glimmer 30B need?
Apple M5 Pro (48GB) has room to spare. At Q4_K_M the weights are ~16.12 GB; with KV cache and runtime overhead, budget ~18.2 GB at a 4k context.
What is the best tool to run Muse Glimmer 30B on macOS?
LM Studio for a simple setup; mlx-lm for the most speed. vLLM is NOT a Mac tool, it is a CUDA/Linux serving engine. Unified memory is not a fixed VRAM slice; ~70% is usable for weights.
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A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/muse-glimmer-30b/apple-m5-pro-48gb) Sources
Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.