Sarvam 30B

Sarvam 30B is a sparse Mixture-of-Experts model with 2.4 billion non-embedding active parameters. Sarvam targets practical deployment, Indian languages, coding, reasoning, conversation, and tool calls. The card also highlights multilingual voice calls. Grouped Query Attention reduces KV-cache pressure, while the 128,000-token window combines long inputs with a focus on more constrained inference environments.

Sarvam’s official release post describes 30B as a production model and documents API access plus downloadable weights. Sarvam 105B follows the same open-model direction at larger scale, but it is a different MoE design. Sarvam documents optimizations for servers, L40S, and Apple Silicon; the card also highlights 22 Indian languages.