Instructions to use sarvamai/sarvam-30b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sarvamai/sarvam-30b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="sarvamai/sarvam-30b", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("sarvamai/sarvam-30b", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sarvamai/sarvam-30b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sarvamai/sarvam-30b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sarvamai/sarvam-30b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sarvamai/sarvam-30b

SGLang

How to use sarvamai/sarvam-30b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "sarvamai/sarvam-30b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sarvamai/sarvam-30b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "sarvamai/sarvam-30b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sarvamai/sarvam-30b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sarvamai/sarvam-30b with Docker Model Runner:
```
docker model run hf.co/sarvamai/sarvam-30b
```

rahular commited on Mar 23

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@@ -84,7 +84,7 @@ The 30B MoE model is designed for throughput and memory efficiency. It uses 19 l
 | 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 |

 | 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) | 88.3 (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 |