Sarvam-30B is an advanced Mixture-of-Experts (MoE) model with 2.4B non-embedding active parameters, designed primarily for practical deployment. It combines strong reasoning, reliable coding ability, and best-in-class conversational quality across Indian languages. Sarvam-30B is built to run reliably in resource-constrained environments and can handle multilingual voice calls while performing tool calls.
A major focus during training was the Indian context and languages, resulting in state-of-the-art performance across 22 Indian languages for its model size.
Sarvam-30B is open-sourced under the Apache License. For more details, see our blog.
Architecture
The 30B MoE model is designed for throughput and memory efficiency. It uses 19 layers, a dense FFN intermediate_size of 8192, moe_intermediate_size of 1024, top-6 routing, grouped KV heads (num_key_value_heads=4), and an extremely high rope_theta (8e6) for long-context stability without RoPE scaling. It has 128 experts with a shared expert, a routed scaling factor of 2.5, and auxiliary-loss-free router balancing. The 30B model focuses on throughput and memory efficiency through fewer layers, grouped KV attention, and smaller experts.
Coding & Knowledge benchmarks (Live Code Bench v6, Arena Hard v2, IF Eval):
Evaluated with temperature=1.0, top_p=1.0, max_new_tokens=65536.
Writing Bench:
Responses generated using official Writing-Bench parameters:
temperature=0.7, top_p=0.8, top_k=20, max_length=16000.
Scoring performed using the official Writing-Bench critic model with:
temperature=1.0, top_p=0.95, max_length=2048.
Agentic benchmarks (BrowseComp, SWE Bench Verified, τ² Bench): Evaluated with temperature=0.5, top_p=1.0, max_new_tokens=32768.
Citation
@misc{sarvam_sovereign_models,
title = {Introducing Sarvam's Sovereign Models},
author = {{Sarvam Foundation Models Team}},
year = {2026},
howpublished = {\url{https://www.sarvam.ai/blogs/sarvam-30b-105b}},
note = {Accessed: 2026-03-03}
}
