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

In a significant stride toward AI innovation tailored for India’s diverse linguistic and technological needs, Bengaluru-based startup Sarvam AI has launched its latest creation: Sarvam-M, a powerful 24-billion-parameter large language model (LLM). Engineered with a sharp focus on Indian languages, mathematics, and programming, Sarvam-M is not only making headlines but also setting benchmarks.

So, what exactly is Sarvam-M, and why is it causing such a stir in the tech community?

🤖 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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