# Muse Spark Open (816B-A42B)

> Meta's multimodal MoE model combines 62 latent-expert blocks with gated GQA, local-only RoPE, global NoPE, and learned residual gates; the figure focuses on the text decoder.

| Property | Value |
| --- | --- |
| Lab | Meta (United States) |
| Released | 2026-10-15 |
| Decoder type | Sparse MoE |
| Family | Sparse MoE |
| Scale | 816B total, 42B active (5.1% active) |
| Context window | 1,048,576 tokens |
| Attention | Gated GQA with scaleless QK-Norm, local-only RoPE, global NoPE, and 3:1 sliding-window/global attention |
| Layer mix | 46 sliding-window + 16 global gated GQA |
| KV cache / token (bf16) | 248 KiB (High) |
| License | Muse Model License |

**Key detail:** Selects 8 of 256 experts, compresses the expert path from 8,192 to 4,096 dimensions, normalizes each expert output before combining, and uses no shared expert.

## Architecture concepts

- [Mixture of Experts (MoE)](https://llm-atlas-7or.pages.dev/concepts/moe.md): Many feed-forward experts, but a router sends each token to only a few of them.
- [Latent MoE (Latent MoE)](https://llm-atlas-7or.pages.dev/concepts/latent-moe.md): Routed experts operate in a smaller latent space instead of the full hidden width.
- [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.
- [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.
- [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.
- [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.
- [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 24.7 (Knowledge Work 37, Coding 7.8, Science 12.1, General 41.7)
- AA Intelligence Index v4.3.2: total 38.9 (General 45, Scientific 29.4, Coding 33.3, Agents 42.8)

## Links

- Architecture diagram: https://sebastianraschka.com/llm-architecture-gallery/images/architectures/muse-spark-open.webp
- config.json: https://huggingface.co/meta-models/Muse-Spark-Open-816B-A42B/blob/main/config.json
- License: https://huggingface.co/meta-models/Muse-Spark-Open-816B-A42B/blob/main/LICENSE.md
- Source card (source of truth): https://sebastianraschka.com/llm-architecture-gallery/#card-muse-spark-open-816b-a42b
- Web page: https://llm-atlas-7or.pages.dev/models/muse-spark-open-816b-a42b/
- JSON: https://llm-atlas-7or.pages.dev/api/models/muse-spark-open-816b-a42b.json

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