sarvam-30b-fp8

Want a bigger model? Download Sarvam-105B!

Index

1. Introduction
2. Architecture
3. Inference
   • SGLang
   • vLLM
4. Citation

Introduction

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.

This repository provides FP8 quantized weights for Sarvam-30B, enabling efficient deployment with reduced memory footprint and faster inference while preserving model quality. The weights are quantized using FP8 (E4M3) format via NVIDIA's ModelOpt toolkit.

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.

Inference

<details> <summary>SGLang</summary>

Install latest SGLang from source

git clone https://github.com/sgl-project/sglang.git
cd sglang
pip install -e "python[all]"

Launch Server

sglang serve --model-path sarvamai/sov_30b_fp8 \
  --port 3002 --host 0.0.0.0 \
  --mem-fraction-static 0.70 \
  --trust-remote-code \
  --tp 2 \
  --enable-dp-attention --dp 2 \
  --prefill-attention-backend fa3 \
  --decode-attention-backend fa3 \
  --ep 2 \
  --tool-call-parser glm45 \
  --reasoning-parser glm45 \
  --quantization modelopt_fp8 \
  --kv-cache-dtype fp8_e4m3

</details> <details> <summary>vLLM</summary>

Note: currently a PR is open for native support for the Sarvam models in vLLM (link). Therefore, we have 2 options here.

Option 1: install from source (hard)

• Use the custom fork here: link
• Follow the instructions here to install from source: link

Option 2: hot-patch (easy)

• Run hotpatch_vllm.py
• This will do the following:
  • install vllm=0.15.0
  • add 2 model entries to registry.py
  • download the model executors for sarvam-105b and sarvam-30b

Once this is done, you can launch the vLLM server.

Important: You must set VLLM_USE_FLASHINFER_MOE_FP8=0 as an environment variable, otherwise the server will get stuck during compilation and crash.

VLLM_USE_FLASHINFER_MOE_FP8=0 vllm serve sarvamai/sarvam-30b-fp8 \
  --trust-remote-code \
  --tensor-parallel-size 2 \
  --quantization modelopt \
  --kv-cache-dtype fp8 \
  --port 3002

</details>

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}
}