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India’s first indigenous AI large language model

Syllabus

GS 3: Achievements of Indians in science & technology

Context:  

Recently, the Government of India has selected Bengaluru-based start-up Sarvam to develop the country's first indigenous artificial intelligence (AI) large language model (LLM) under the IndiaAI Mission.

More on the News

  • In February, during the Paris AI Summit, Prime Minister Narendra Modi stated that India is on its way to developing its Large Language Model (LLM). 

• The selection comes amid the global rise of China’s low-cost LLM DeepSeek, which has disrupted the AI industry.

  • DeepSeek entered the AI space with an open-source model that’s known for being accurate and much cheaper to build than its U.S. competitors.

• The '2022 Unesco State of the Education Report' stated that India has the highest relative AI skill penetration rate in the world. 

• The '2024 Stanford AI Index Report' places India among the top countries in the AI advancement world.

Selection & Support

  • Sarvam was chosen from among 67 applicants under the ₹10,370 crore IndiaAI Mission.
  • It is the first start-up approved for government incentives (SOPs) under this mission.

The government will provide:

  • Access to 4,000 Graphics Processing Units (GPUs) for six months.
  • Compute infrastructure via selected Indian companies setting up AI data centres.

• The company is developing three model variants: 

  • Sarvam-Large – For advanced reasoning and text generation.
  • Sarvam-Small – For real-time interactive applications.
  • Sarvam-Edge – Designed for compact, on-device use cases.

• Sarvam said its model will be capable of reasoning, designed for voice, and fluent in Indian languages, and it will be ready for population-scale deployment.

IndiaAI Mission

  • It was launched in 2024, when the Union cabinet approved over Rs 10,300 crore for the IndiaAI Mission to strengthen the Artificial Intelligence (AI) Innovation Ecosystem.
  • It aims to foster AI innovation by democratizing computing access, enhancing data quality, developing indigenous AI capabilities, and attracting top AI talent.

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