# Muse Glimmer (30B)

> Meta's dense 29.6B multimodal model combines a 52-layer sandwich-norm decoder with gated GQA and a separate perception encoder; the figure focuses on the text backbone.

| Property | Value |
| --- | --- |
| Lab | Meta (United States) |
| Released | 2026-08-10 |
| Decoder type | Dense |
| Family | Dense |
| Scale | 29.6B parameters (including perception encoder) |
| Context window | 131,072+ tokens |
| Attention | Gated GQA with scaleless QK-Norm, local-only RoPE, global NoPE, and 3:1 sliding-window/global attention |
| Layer mix | 39 sliding-window + 13 global gated GQA |
| KV cache / token (bf16) | 52 KiB (Low) |
| License | Apache License 2.0 |

**Key detail:** Applies RoPE only in 2,048-token local layers, uses NoPE in global layers, and gates the attention output before projection.

## Architecture concepts

- [Grouped-Query Attention (GQA)](https://llm-atlas-7or.pages.dev/concepts/gqa.md): Several query heads share one key/value head, shrinking the KV cache with little quality loss.
- [QK-Norm (QK-Norm)](https://llm-atlas-7or.pages.dev/concepts/qk-norm.md): RMSNorm applied to queries and keys before attention to keep logits well-behaved.
- [Sliding Window Attention (SWA)](https://llm-atlas-7or.pages.dev/concepts/swa.md): Some layers only attend to a local window of recent tokens instead of the whole context.
- [Gated Attention (Gated attention)](https://llm-atlas-7or.pages.dev/concepts/gated-attention.md): A learned sigmoid gate scales the attention output before it re-enters the residual stream.
- [No Positional Embeddings (NoPE)](https://llm-atlas-7or.pages.dev/concepts/nope.md): Some or all layers drop explicit positional encodings and rely on the causal mask.

## Benchmark (Artificial Analysis Intelligence Index)

- AA Intelligence Index v5.0: total 6.3 (Knowledge Work 3.4, Coding 1.1, Science 2.8, General 18.1)
- AA Intelligence Index v4.3.2: total 17.5 (General 29.2, Scientific 12.3, Coding 22.7, Agents 5.9)

## Links

- Architecture diagram: https://sebastianraschka.com/llm-architecture-gallery/images/architectures/muse-glimmer-30b.webp
- config.json: https://huggingface.co/meta-models/Muse-Glimmer-30B/blob/main/config.json
- License: https://huggingface.co/meta-models/Muse-Glimmer-30B/blob/main/LICENSE
- Tech report: https://research.meta.ai/static/muse-glimmer-methodology
- Source card (source of truth): https://sebastianraschka.com/llm-architecture-gallery/#card-muse-glimmer-30b
- Web page: https://llm-atlas-7or.pages.dev/models/muse-glimmer-30b/
- JSON: https://llm-atlas-7or.pages.dev/api/models/muse-glimmer-30b.json

_Data from Sebastian Raschka's LLM Architecture Gallery (https://sebastianraschka.com/llm-architecture-gallery/). Synced 2026-11-04._