# Sarvam (30B)

> Reasoning-oriented Indian-language sparse MoE that keeps GQA at the smaller size.

| Property | Value |
| --- | --- |
| Lab | Sarvam AI (India) |
| Released | 2026-03-03 |
| Decoder type | Sparse MoE |
| Family | Sparse MoE |
| Scale | 30B total, 2.4B active (8% active) |
| Context window | 131,072 tokens |
| Attention | GQA with QK-Norm |
| Layer mix | 19 GQA |
| KV cache / token (bf16) | 19 KiB (Very low) |
| License | Apache License 2.0 |

**Key detail:** Large vocabulary and strong Indic language support paired with a reasoning-focused sparse MoE design.

## Architecture concepts

- [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.
- [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 6.6 (Scientific 3.9)

## Links

- Architecture diagram: https://sebastianraschka.com/llm-architecture-gallery/images/architectures/sarvam-30b.webp
- View in article: https://magazine.sebastianraschka.com/p/a-dream-of-spring-for-open-weight#%C2%A7update-1-sarvam-30b-and-105b-mar-6-2026
- config.json: https://huggingface.co/sarvamai/sarvam-30b/blob/main/config.json
- Tech report: https://www.sarvam.ai/blogs/sarvam-30b-105b
- Source card (source of truth): https://sebastianraschka.com/llm-architecture-gallery/#card-sarvam-30b
- Web page: https://llm-atlas-7or.pages.dev/models/sarvam-30b/
- JSON: https://llm-atlas-7or.pages.dev/api/models/sarvam-30b.json

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