Indian AI Startup Launches Sarvam-M Model: What Is It, Why Is Everyone Talking About It 

Bengaluru-based startup Sarvam AI has introduced Sarvam-M, a potent 24-billion-parameter large language model (LLM), marking a major advancement in AI innovation catered to India’s varied linguistic and technological needs. Sarvam-M, which was designed with a keen interest in Indian languages, maths, and programming, is not only generating news but also establishing standards.

So, what is Sarvam-M exactly, and why is the tech community so excited about it?

What Is Sarvam-M?

Sarvam-M is a multilingual, open-source AI model based on the Mistral Small architecture, trained with a specialized focus on:

• 11 languages: Hindi, Tamil, Bengali, Telugu, and other major Indian languages, along with English.

• Mathematics and programming tasks

• Reasoning and natural language understanding

With 24 billion parameters, Sarvam-M balances power and efficiency, delivering results comparable to larger models while maintaining lean inference capabilities.

Why Is Everyone Talking About It?

1. Tailored for India

Most global AI models struggle with Indian languages due to data inefficiencies and tokenization issues. Sarvam-M breaks that mold by scoring 0.75 on MILU-IN, outperforming baseline models like Mistral’s original 7B and 8B variants.

2. Multimodal Thinking

Sarvam-M introduces two unique inference modes:

• Non-think Mode for quick, reactive outputs

• Think Mode for complex tasks like reasoning and coding

This dual-mode functionality makes it versatile for both casual queries and advanced workflows.

3. Strong Benchmark Performance

Sarvam-M significantly outperforms its peers on:

• GSM-8K (Math): 0.94 score

• HumanEval (Programming): 0.88 score

• IndicBench (Languages): 20% higher than Mistral base

4. Open Source & Efficient

Trained and optimized on Indian infrastructure, it is fully open-source and FP8 quantized, meaning faster inference with minimal performance loss. It’s freely accessible on Hugging Face, encouraging wide adoption.

How It Works: Under the Hood

• Supervised Fine-Tuning (SFT): Using quality-scored prompts for better contextual learning.

• Reinforcement Learning with Verifiable Rewards (RLVR): Fine-tuned with tasks in reasoning, coding, and language.

• FP8 Quantization: Post-training quantization enables rapid deployment on hardware like NVIDIA H100 GPUs.

Real-World Applications

It is poised to power next-gen AI solutions across sectors:

• Conversational agents in regional languages

• Voice-based interfaces in public services

• Translation engines

• EdTech platforms for math and coding education

• Customer support automation in multilingual settings

Final Thoughts

Sarvam-M is more than just a new AI model; it’s India’s bold step toward self-reliant AI infrastructure, tailored to its people and problems. As global players focus on scaling size, Sarvam AI is focused on scaling relevance and accessibility.

If you’re a developer, researcher, or business working with Indian languages or looking for AI in education, it might just be the model you’ve been waiting for.

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Frequently Asked Questions (FAQ)

1. What is Sarvam M?
Sarvam-M is a 24-billion-parameter multilingual large language model (LLM) developed by Bengaluru-based startup Sarvam AI. It’s designed with a focus on Indian languages, mathematics, and programming.

2. Which languages does Sarvam M support?
It supports 11 major languages, including Hindi, Tamil, Bengali, Telugu, and English, among others.

3. What makes Sarvam-M different from other AI models?
Unlike most global models that struggle with Indian languages, Sarvam-M is specifically optimized for India’s linguistic diversity and performs exceptionally well on Indic benchmarks.

4. What are the key technical features of Sarvam M?

• 24 billion parameters for balanced power and efficiency

• Dual inference modes: Non-think Mode (fast responses) and Think Mode (deep reasoning and coding)

• FP8 quantization for faster, more efficient inference

• Open-source and available on Hugging Face

5. How does Sarvam M perform on benchmarks?
It outperforms similar-sized models with:

• GSM-8K (Math): 0.94 score

• HumanEval (Programming): 0.88 score

• IndicBench (Languages): 20% higher than Mistral base models