# Llama 4 Maverick (400B)

> Meta's large MoE follows the DeepSeek V3 playbook but with a more conventional attention stack.

| Property | Value |
| --- | --- |
| Lab | Meta (United States) |
| Released | 2025-04-05 |
| Decoder type | Sparse MoE |
| Family | Sparse MoE |
| Scale | 400B total, 17B active (4.3% active) |
| Context window | 1,000,000 tokens |
| Attention | GQA |
| Layer mix | 36 chunked + 12 full GQA |
| KV cache / token (bf16) | 192 KiB (High) |
| License | Llama 4 Community License Agreement |

**Key detail:** Alternates dense and MoE blocks and uses fewer, larger experts than DeepSeek V3.

## 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.
- [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.

## Benchmark (Artificial Analysis Intelligence Index)

- AA Intelligence Index v4.3.2: total 9.3 (General 18.8, Scientific 2.5, Coding 15.9, Agents 0)

## Links

- Architecture diagram: https://sebastianraschka.com/llm-architecture-gallery/images/architectures/llama-4-maverick-400b.webp
- View in article: https://magazine.sebastianraschka.com/p/the-big-llm-architecture-comparison#%C2%A75-llama-4
- config.json: https://huggingface.co/meta-llama/Llama-4-Maverick-17B-128E-Instruct/blob/main/config.json
- Tech report: https://ai.meta.com/blog/llama-4-multimodal-intelligence/
- Source card (source of truth): https://sebastianraschka.com/llm-architecture-gallery/#card-llama-4-maverick
- Web page: https://llm-atlas-7or.pages.dev/models/llama-4-maverick/
- JSON: https://llm-atlas-7or.pages.dev/api/models/llama-4-maverick.json

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