abhinand/sarvam-30b-bf16

The original model was converted from FP32 to mixed precision, with most weights cast to BF16 for memory and inference efficiency while keeping the MoE router (MoEGate) in FP32 to preserve routing stability and avoid precision-related issues.

Want a bigger model? Download Sarvam-105B!

Index

1. Introduction
2. Architecture
3. Benchmarks
   • Knowledge & Coding
   • Reasoning & Math
   • Agentic
4. Inference
   • Hugging Face
   • vLLM
5. Footnote
6. 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.

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.

Benchmarks

Knowledge & Coding

Benchmark	Sarvam-30B	Gemma 27B It	Mistral-3.2-24B	OLMo 3.1 32B Think	Nemotron-3-Nano-30B-A3B	Qwen3-30B-Thinking-2507	GLM 4.7 Flash	GPT-OSS-20B
Math500	97.0	87.4	69.4	96.2	98.0	97.6	97.0	94.2
HumanEval	92.1	88.4	92.9	95.1	97.6	95.7	96.3	95.7
MBPP	92.7	81.8	78.3	58.7	91.9	94.3	91.8	95.3
Live Code Bench v6	70.0	28.0	26.0	73.0	68.3	66.0	64.0	61.0
MMLU	85.1	81.2	80.5	86.4	84.0	88.4	86.9	85.3
MMLU Pro	80.0	68.1	69.1	72.0	78.3	80.9	73.6	75.0
MILU	76.8	69.2	67.9	69.9	64.8	82.6	75.6	73.7
Arena Hard v2	49.0	50.1	43.1	42.0	67.7	72.1	58.1	62.9
Writing Bench	78.7	71.4	70.3	75.7	83.7	85.0	79.2	79.1

Reasoning & Math

Benchmark	Sarvam-30B	OLMo 3.1 32B	Nemotron-3-Nano-30B	Qwen3-30B-Thinking-2507	GLM 4.7 Flash	GPT-OSS-20B
GPQA Diamond	66.5	57.5	73.0	73.4	75.2	71.5
AIME 25 (w/ Tools)	80.0 (96.7)	78.1 (81.7)	89.1 (99.2)	85.0 (-)	91.6 (-)	91.7 (98.7)
HMMT (Feb 25)	73.3	51.7	85.0	71.4	85.0	76.7
HMMT (Nov 25)	74.2	58.3	75.0	73.3	81.7	68.3
Beyond AIME	58.3	48.5	64.0	61.0	60.0	46.0

Agentic

Benchmark	Sarvam-30B	Nemotron-3-Nano-30B	Qwen3-30B-Thinking-2507	GLM 4.7 Flash	GPT-OSS-20B
BrowseComp	35.5	23.8	2.9	42.8	28.3
SWE Bench Verified	34.0	38.8	22.0	59.2	34.0
τ² Bench (avg.)	45.7	49.0	47.7	79.5	48.7

See footnote for evaluation details.

Inference

Huggingface

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig

model_name = "abhinand/sarvam-30b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, device_map="auto")

def generate_text(
    prompt: str,
    max_new_tokens: int = 2048,
    temperature: float = 0.8,
    top_p: float = 0.95,
    repetition_penalty: float = 1.0,
) -> None:
    inputs = tokenizer(prompt, return_tensors="pt").to("cuda:0")

    generation_config = GenerationConfig(
        max_new_tokens=max_new_tokens,
        repetition_penalty=repetition_penalty,
        temperature=temperature,
        top_p=top_p,
        do_sample=True,
    )

    with torch.no_grad():
        output_ids = model.generate(
            input_ids=inputs["input_ids"],
            attention_mask=inputs["attention_mask"],
            generation_config=generation_config,
        )
    return tokenizer.decode(output_ids[0], skip_special_tokens=True)

prompts = [
    "What is the capital city of New Zealand?",
]

for prompt in prompts:
    templated_prompt = tokenizer.apply_chat_template(
      [{"role": "user", "content": prompt}],
      tokenize=False,
      add_generation_prompt=True,
      enable_thinking=True
    )
    output = generate_text(templated_prompt, max_new_tokens=512)
    print("Prompt: ", prompt)
    print("Generated text: ", output)
    print("=" * 100)

SGLang

Install latest SGLang from source

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

Instantiate model and Run

import sglang as sgl
from transformers import AutoTokenizer

model_path = "abhinand/sarvam-30b"
engine = sgl.Engine(
    model_path=model_path,
    tp_size=2,
    mem_fraction_static=0.8,
    trust_remote_code=True,
    dtype="bfloat16",
    prefill_attention_backend="fa3",
    decode_attention_backend="fa3",
)

sampling_params = {
    "temperature": 0.8,
    "max_new_tokens": 2048,
    "repetition_penalty": 1.0,
}

prompts = [
    "Which treaty formally ended World War I and imposed heavy reparations on Germany?",
]

outputs = engine.generate([
    tokenizer.apply_chat_template([
        {"role": "user", "content": prompt}],
        tokenize=False,
        add_generation_prompt=True,
        enable_thinking=True)
        for prompt in prompts], 
    sampling_params)
for p, o in zip(prompts, outputs):
    print("Prompt: ", p)
    print("Generated text: ", o['text'])
    print("=" * 100)

vLLM

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 run vLLM as usual

from vllm import LLM, SamplingParams
from transformers import AutoTokenizer

model_path = "abhinand/sarvam-30b"
tokenizer = AutoTokenizer.from_pretrained(model_path)
llm = LLM(model=model_path, 
            trust_remote_code=True, 
            max_model_len=2048, 
            tensor_parallel_size=8, 
            max_num_seqs=16,
        )
sampling_params = SamplingParams(
                    temperature=0.8, 
                    max_tokens=2048, 
                    repetition_penalty=1.0,
                    spaces_between_special_tokens=True
                )

prompts = [
    "Who wrote The Picture of Dorian Gray?",
]

outputs = llm.generate([
    tokenizer.apply_chat_template([
        {"role": "user", "content": prompt}],
        tokenize=False,
        add_generation_prompt=True,
        enable_thinking=True)
        for prompt in prompts], 
    sampling_params)
for p, o in zip(prompts, outputs):
    print("Prompt: ", p)
    print("Generated text: ", o.outputs[0].text)
    print("=" * 100)

Footnote

• General settings: All benchmarks are evaluated with a maximum context length of 65,536 tokens.
• Reasoning & Math benchmarks (Math500, MMLU, MMLU Pro, GPQA Diamond, AIME 25, Beyond AIME, HMMT, HumanEval, MBPP): Evaluated with temperature=1.0, top_p=1.0, max_new_tokens=65536.
• 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}
}