What Happened
- Sarvam AI, a Bengaluru-based AI startup, unveiled two large language models (LLMs) at the India AI Impact Summit 2026 — Sarvam-30B and Sarvam-105B — both built and trained from scratch in India
- Sarvam AI was selected by MeitY under the IndiaAI Mission's Innovation Centre pillar to develop an indigenous foundational AI model, receiving government support of ₹246.72 crore
- The models are designed specifically for Indian languages and public service delivery, supporting advanced reasoning, multilingual tasks, mathematics, and coding
- Union Minister Shri Amit Shah stated at the summit that Sarvam AI "exemplifies why the future belongs to India"
- PM Modi publicly lauded Sarvam AI alongside other indigenous AI models as proof of India's innovative capability
- Sarvam AI's approach is defined as "sovereign AI" — development, deployment, and governance remaining entirely within India, using Indian compute infrastructure
Static Topic Bridges
Sarvam AI — Technical Architecture and Capabilities
Sarvam AI is an Indian AI company co-founded by IIT-Madras alumni Vivek Raghavan and Pratyush Kumar. It operates as a full-stack AI company: building its own foundational language models, speech models, and application layer products. Both models announced at the AI Impact Summit use Mixture of Experts (MoE) architecture — a design that activates only a fraction of parameters per inference, enabling large model scale while remaining computationally efficient.
- Sarvam-30B: 30 billion parameter model, Mixture of Experts design
- Sarvam-105B: 105 billion parameter model; activates ~9 billion parameters per token; 128,000-token context window
- Architecture: Mixture of Experts (MoE) — enables scale without proportional compute cost increase
- Language support: 22 scheduled Indian languages plus English; focus on Indic linguistic nuances
- Training: From scratch on India-centric datasets (not fine-tuned Western models)
- Use cases: Voice-based citizen interfaces, government document processing, multilingual chatbots, coding assistants
- Speech models: Sarvam ASR (automatic speech recognition) and TTS (text-to-speech) for Indic languages
Connection to this news: Sarvam AI's foundational model launch marks India's transition from AI consumer and application builder to AI infrastructure producer — the models are trained on Indian data, in India, for Indian needs, addressing the fundamental dependency on foreign models for sovereign deployment.
IndiaAI Mission — Innovation Centre and Foundational Model Programme
The IndiaAI Mission's Innovation Centre pillar funds the development of indigenous foundational AI models. Following a competitive selection process, MeitY shortlisted 12 teams from across academia, startups, and research institutions. Sarvam AI received ₹246.72 crore in government support — primarily computing credits on the IndiaAI compute pool (38,000+ GPUs across 14 cloud providers). This model of public compute as a subsidy for AI development mirrors France's AI Compute programme and the US NSF National AI Research Resource.
- IndiaAI Mission total budget: ₹10,300 crore; approved by CCEA, March 2024
- Innovation Centre: 12 teams selected for foundational model development
- Sarvam AI government support: ₹246.72 crore (compute + development support)
- BharatGen Param2 funding: ₹988.6 crore (largest single allocation)
- GPU infrastructure: 38,000 GPUs via 14 cloud service providers; data centres in Mumbai, Navi Mumbai, Hyderabad, Bengaluru, Noida, Jamnagar
- Nodal agency: Digital India Corporation (DIC) under MeitY
- BHASHINI integration: Sarvam's language models feed into the national BHASHINI platform
Connection to this news: Sarvam AI's ₹246.72 crore government allocation enabled it to train large-scale foundational models on domestic compute rather than relying on OpenAI or Google APIs — the IndiaAI Mission's compute pool is the direct material enabler of its "Made in India" claim.
Large Language Models (LLMs) — Foundational Technology Concepts
A Large Language Model (LLM) is a neural network trained on massive text datasets to perform natural language tasks. Foundation models are large, general-purpose models trained at scale that can be fine-tuned for specific applications (question-answering, translation, code generation, document analysis). The Mixture of Experts (MoE) architecture, pioneered at scale by models like Mistral Mixtral and Google Gemini 1.5, uses a "router" to activate only a subset of specialised "expert" sub-networks for each input, dramatically reducing inference compute while retaining large model capacity.
- Parameter count and capability: Larger models generally more capable; GPT-4 estimated at 1.8 trillion parameters; Sarvam-105B comparable to mid-range international models
- MoE vs Dense models: Dense models activate all parameters per token; MoE activates only a fraction (Sarvam-105B activates ~9B of 105B per token)
- Context window: Sarvam-105B has 128,000-token window — equivalent to ~90,000 words; enables processing of long documents
- Training data: Quality and diversity of training data (not just scale) determines model quality for specific languages and domains
- Evaluation benchmarks: MMLU (general knowledge), HumanEval (coding), IndicEval (Indian language tasks)
Connection to this news: Understanding MoE architecture is key to appreciating why Sarvam's 105B-parameter model is computationally viable on India's current GPU infrastructure — MoE reduces the effective compute cost of inference, making large Indic-language models practically deployable at government scale.
Key Facts & Data
- Company: Sarvam AI, Bengaluru; co-founders include IIT Madras alumni Vivek Raghavan and Pratyush Kumar
- Models unveiled: Sarvam-30B and Sarvam-105B (both MoE architecture, trained from scratch in India)
- Sarvam-105B: 105B parameters; ~9B activated per token; 128,000-token context window
- Government support: ₹246.72 crore under IndiaAI Mission's Innovation Centre
- IndiaAI Mission budget: ₹10,300 crore; GPU pool: 38,000+ (expanding to 58,000+)
- Languages: 22 scheduled Indian languages + English
- Summit recognition: Amit Shah — "exemplifies why the future belongs to India"; PM Modi publicly lauded
- BharatGen Param2 (comparator model): 17B parameters; ₹988.6 crore funding; 22 Indian languages
- BHASHINI: National language AI platform that Sarvam models feed into