Run Meta's Muse Glimmer 30B locally with Lemonade - Lemonade Server
Author: Lemonade Team
Description: Meta Superintelligence Labs released Muse Glimmer 30B under Apache 2.0. Run it locally with Lemonade on AMD, NVIDIA, Intel, and ARM GPUs in three commands.
Date: 2026-08-10

New model Apache 2.0 August 10, 2026

# Run Meta's Muse Glimmer 30B with Lemonade

Meta Superintelligence Labs released Muse Glimmer 30B today: a dense, multimodal, open-weights model trained for agentic work rather than one-shot answers. It reads screenshots and documents, calls tools, and manages its own memory, so a long multi-step task can pick up where it left off instead of starting over. Lemonade runs it locally in three commands, and serves it over the OpenAI-, Ollama-, and Anthropic-compatible APIs your agents already speak.

Parameters
  30B dense
Input
  Text and vision
License
  Apache 2.0
Download at 4-bit
  About 20 GB
Runs on
  32 GB+ GPUs

## Setup

Set aside about 20 GB of free disk space for the download.

1

### Point llama.cpp at the latest release

Muse Glimmer is a new architecture, so it needs the upstream llama.cpp build that added support for it. Run the one that matches your hardware.

# vulkan: cross-platform, covers AMD, NVIDIA, Intel, and ARM GPUs `lemonade config set llamacpp.vulkan_bin=latest`
# rocm: optimized for AMD `lemonade config set llamacpp.rocm_bin=latest`
# cuda: optimized for NVIDIA `lemonade config set llamacpp.cuda_bin=latest`

Note: Set Lemonade to use any backend your hardware supports with the `llamacpp` config, for example `lemonade config set llamacpp.backend=rocm`.

Note: Set the same key to `builtin` to return to the llama.cpp version Lemonade ships and tests, for example `lemonade config set llamacpp.vulkan_bin=builtin`.

Note: Muse Glimmer ships as a built-in model in Lemonade v11.6.0, so this step becomes optional once you update.

2

### Pull the weights

We like to recommend the 4-bit Unsloth Dynamic quantization for local use. Other sizes, from 2-bit up to BF16, are available in the same Unsloth repository. Lemonade picks up the vision projector from that repo automatically.

# pull: downloads about 20 GB, weights plus vision projector `lemonade pull unsloth/Muse-Glimmer-30B-GGUF:UD-Q4_K_XL`
3

### Use the model

Lemonade registers the download as `Muse-Glimmer-30B-GGUF-UD-Q4_K_XL`, combining the repository name with the quantization you chose. Use that name with whichever of these fits where you want to work.

#### Serve the endpoint to apps and agents

`lemonade load Muse-Glimmer-30B-GGUF-UD-Q4_K_XL`

#### Open it in the web app

`lemonade run Muse-Glimmer-30B-GGUF-UD-Q4_K_XL`

#### Chat in your terminal

`lemonade chat Muse-Glimmer-30B-GGUF-UD-Q4_K_XL`

#### Use it in a coding agent

Claude Code, Codex, and OpenCode work here too.

`lemonade launch pi --model Muse-Glimmer-30B-GGUF-UD-Q4_K_XL`

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