Sarvam AI’s 105B Model: How India Built a Smarter, More Efficient Alternative to Google and OpenAI
In a historic moment for Indian technology, Sarvam AI has unveiled what may be the most important AI model ever built on Indian soil—and it’s outperforming global giants on the tasks that matter most for 1.4 billion people.
The flagship release includes the Sarvam-105B (a highly efficient Mixture-of-Experts model with 105 billion total parameters and approximately 9 billion active parameters) and the lighter Sarvam-30B variant. Both were trained from scratch on Indian compute infrastructure under the IndiaAI Mission .
But here’s what makes this announcement truly groundbreaking: Sarvam’s models aren’t just bigger—they’re smarter for Bharat .
What Makes Sarvam Different: A Side-by-Side Comparison
To understand Sarvam’s significance, it helps to compare directly with the global alternatives that Indian developers and enterprises have relied on until now:
| Capability | Sarvam-105B | Google Gemini | OpenAI GPT-4/4o | Anthropic Claude |
|---|---|---|---|---|
| Indic Language Support | Native-level across 22 scheduled languages | Limited, often inconsistent | Poor for low-resource languages | Minimal |
| Code-Mixing (Hinglish, etc.) | Excellent (trained on real Indian conversations) | Weak | Poor | Poor |
| Cultural Context | Deep understanding of idioms, festivals, social norms | Superficial | Superficial | Minimal |
| Inference Cost | Dramatically lower (optimized for efficiency) | High | Very High | High |
| Sovereignty | Fully Indian (trained on domestic compute) | Foreign | Foreign | Foreign |
| Open Source | Yes (weights, inference code, fine-tuning recipes) | No | No | No |
| Performance on Indic Benchmarks | Outperforms larger models | Weaker | Weaker | Weaker |
This isn’t just a matter of national pride—it’s about building AI that actually works for Indian users .
The Technical Breakthrough: Efficiency Through Architecture
Mixture-of-Experts (MoE) Design
Sarvam-105B uses a Mixture-of-Experts (MoE) architecture, which activates only a fraction of its total parameters for each task:
- Total parameters: 105 billion
- Active parameters per inference: ~9 billion
- Context window: 128,000 tokens (ideal for complex reasoning and long-form content)
This design delivers frontier-level results at dramatically lower inference cost—critical for scalable deployment across India’s diverse digital economy .
Sarvam-30B: The Real-Time Companion
The smaller variant offers:
- 30 billion total parameters
- 32,000-token context window
- Optimized for low-latency, real-time conversational use cases
Training Infrastructure
Crucially, both models were trained entirely on Indian compute infrastructure under the IndiaAI Mission, utilizing GPU resources allocated through partners like Yotta Data Services . This ensures:
- Data sovereignty —training data never leaves Indian jurisdiction
- Strategic independence —reducing reliance on foreign cloud providers
- Ecosystem development —building domestic AI infrastructure expertise
Native-Level Performance Across 22 Indian Languages
This is where Sarvam separates itself from every global alternative.
The Full Spectrum
Sarvam’s models deliver exceptional performance across all 22 scheduled Indian languages:
- Hindi (and dialects like Bhojpuri, Rajasthani)
- Bengali
- Tamil
- Telugu
- Marathi
- Gujarati
- Kannada
- Malayalam
- Punjabi
- Odia
- Assamese
- And more
Code-Mixing and Real-World Inputs
Perhaps more impressive is the handling of code-mixing—the natural blending of languages in everyday Indian speech:
- Hinglish (Hindi+English)
- Tanglish (Tamil+English)
- Benglish (Bengali+English)
- Other regional combinations
Global models trained primarily on English internet data struggle with this. Sarvam’s models, trained on real Indian conversations, handle it naturally.
Noisy Inputs
Real-world AI deployment means dealing with:
- Voice recognition errors (for voice-first interfaces)
- Typographical errors (especially in non-Latin scripts)
- Incomplete sentences
- Dialect variations
Sarvam’s training data includes these real-world inputs, making the models robust in production environments .
Benchmark Dominance: Beating Larger Models
Sarvam-105B isn’t just competitive—it’s outperforming much larger models on the benchmarks that matter for India:
| Benchmark Category | Sarvam-105B Performance | Comparison |
|---|---|---|
| Indian-language reasoning | Superior | Beats DeepSeek R1 (671B) and Gemini 2.5 Flash variants |
| Technical tasks | Superior | Outperforms models 6x larger |
| Coding | State-of-the-art | Competitive with leading coding-specialized models |
| Agentic workflows | Superior | Better tool-calling and multi-step reasoning |
| Cost-efficiency | Dramatically better | Lower inference cost than Gemini Flash |
As co-founder Pratyush Kumar put it: “We’re not trying to copy global models—we’re building AI that truly understands and serves 1.4 billion Indians. Sovereign, multilingual, and practical—that’s the Indian way.”
Enterprise-First Design: Built for Real-World Impact
Sarvam’s models aren’t academic exercises—they’re designed for high-stakes enterprise deployment across critical sectors:
1. Software Modernization
- Legacy code analysis and migration
- Documentation generation
- Test automation
- Architecture recommendations
2. Legal Document Analysis
- Contract review and summarization
- Compliance checking
- Case law research
- Drafting assistance (in multiple languages)
3. Healthcare Diagnostics
- Symptom analysis and triage
- Medical record summarization
- Clinical decision support
- Patient education in local languages
4. Agricultural Advisory
- Crop selection guidance
- Pest and disease identification
- Weather-based recommendations
- Market price information
5. Multilingual Customer Support
- Automated response in customer’s preferred language
- Sentiment analysis
- Escalation routing
- Quality monitoring
6. Governance Tools
- Citizen query resolution
- Document processing
- Scheme eligibility checking
- Grievance redressal
7. Voice-First Agents
- Conversational interfaces for low-literacy users
- Integration with telephony systems
- Real-time translation
- Natural dialogue handling
Sovereignty and Open Access
Open Source Commitment
In a move that distinguishes Sarvam from most global competitors, the company has released:
- Model weights under permissive licenses
- Inference code for easy deployment
- Fine-tuning recipes for customization
- Documentation and examples
This empowers:
- Developers to build India-first applications without foreign dependencies
- Startups to innovate on top of world-class foundational models
- Researchers to study and improve the models
- Enterprises to deploy with full control
Data Sovereignty
Because the models were trained on Indian infrastructure:
- Training data remains within Indian jurisdiction
- Deployment can happen entirely on domestic servers
- Compliance with DPDP Act is simplified
- Strategic independence from foreign cloud providers
The IndiaAI Mission Connection
Sarvam was among the first startups selected under the IndiaAI Mission to build indigenous foundational models . The mission provided:
- Compute subsidies through Yotta Data Services
- Ecosystem support and mentorship
- Validation of sovereign AI approach
Cultural & Contextual Intelligence
This is perhaps the most subtle but important differentiator.
What Global Models Miss
When a user says:
- “Beta, thoda adjust kar lo”
- “Chalo, chai pe charcha karte hain”
- “Mausi aaj kal kyun nahi a rahi?”
Global models trained on English-language datasets miss the cultural subtext. They don’t understand:
- Family dynamics and relationship hierarchies
- Festival-specific customs and expressions
- Regional variations in customs and language
- Social norms around politeness, respect, and indirectness
What Sarvam Understands
Sarvam’s training data includes:
- Indian literature, media, and conversations
- Regional language content across domains
- Real-world interactions reflecting everyday life
- Cultural context that makes interactions natural
The result: interactions feel natural and trustworthy to Indian users.
The Efficiency Edge: Why Cost Matters
The Inference Cost Problem
Global models like GPT-4 and Gemini are expensive to run:
- High API costs per token
- Significant compute requirements
- Often requires specialized hardware
This makes them prohibitively expensive for population-scale deployment in price-sensitive markets.
Sarvam’s Solution
Sarvam-105B’s MoE architecture delivers:
- Dramatically lower inference costs (fraction of GPT-4 or Gemini)
- Optimized for Indian infrastructure and conditions
- Scalable deployment across millions of users
- Viable economics for B2B and B2C applications
As Pratyush Kumar emphasized: “Efficiency isn’t a nice-to-have—it’s essential for serving India at scale.”
The Ecosystem Impact
Sarvam’s release creates ripple effects across India’s AI ecosystem:
For Startups
- Free, world-class foundation model to build upon
- Lower barriers to entry for AI-native companies
- Differentiation through fine-tuning for specific domains
- Credibility with investors and customers
For Enterprises
- Sovereign AI option reducing foreign dependency
- Lower costs for AI deployment at scale
- Better performance on India-specific use cases
- Full control over data and deployment
For Developers
- Access to cutting-edge models without API keys or credit cards
- Open-source code to learn and experiment
- Community of builders working on Indian AI
- Career opportunities in India’s AI ecosystem
For Researchers
- Model weights for study and improvement
- Benchmarking against global alternatives
- Collaboration with Sarvam and ecosystem
- Publications and recognition
For Government
- Validation of IndiaAI Mission approach
- Sovereign capability in critical technology
- Reduced dependency on foreign platforms
- Template for future foundational model efforts
The Global Context: India’s AI Moment
Sarvam’s launch comes amid extraordinary momentum:
Recent Milestones (February 2026)
| Announcement | Significance |
|---|---|
| Sarvam-105B | India’s most powerful foundational model |
| Yotta $2B supercluster | 20,736 NVIDIA Blackwell GPUs, massive compute expansion |
| NVIDIA-AIGI partnership | Catalyzing 10,000+ builders and 500+ AI ventures |
| General Catalyst $5B commitment | Historic VC pledge to Indian tech |
| Peak XV $1.3B fund | Major domestic VC firepower |
| Lightspeed $1.3B fund | 60% allocation to applied AI |
| Pax Silica membership | India joins global tech alliance |
| New Delhi AI Commitments | Framework for responsible AI |
| PM Modi’s MANAV Vision | Ethical AI governance framework |
The Pattern
Each of these developments reinforces the others:
- Compute infrastructure (Yotta) enables model development (Sarvam)
- Model development creates demand for tools and platforms (NVIDIA-AIGI)
- Tools empower startups (Peak XV, Lightspeed, General Catalyst)
- Startups drive adoption and demonstrate value
- Value attracts investment and global partnerships (Pax Silica)
- Policy frameworks (MANAV Vision, New Delhi Commitments) provide guidance
Sarvam sits at the center of this virtuous cycle.
What This Means for Different Audiences
For Indian Developers
- Experiment with Sarvam-105B—download the weights, run inference locally
- Build India-first applications that global models can’t handle
- Contribute to the ecosystem through open-source tools and fine-tuned models
- Join the community of builders shaping Indian AI
For Indian Startups
- Build on Sarvam’s foundation rather than starting from scratch
- Differentiate through domain expertise and fine-tuning
- Access global capital with a sovereign AI story
- Scale cost-effectively with efficient inference
For Enterprises
- Pilot Sarvam-based solutions for India-specific use cases
- Reduce dependency on foreign AI platforms
- Control costs with efficient deployment
- Ensure compliance with data sovereignty requirements
For Global Companies
- Partner with Sarvam for India market entry
- Learn from India’s approach to multilingual, efficient AI
- Respect sovereignty by deploying Indian models for Indian users
- Collaborate on open-source initiatives
For Policymakers
- Celebrate and amplify this success story
- Continue supporting foundational model development
- Expand compute access through IndiaAI Mission
- Promote adoption across government and public sector
The Road Ahead for Sarvam
Near-Term Priorities
- Community building around open-source release
- Enterprise partnerships for deployment at scale
- Continuous improvement of model capabilities
- Ecosystem development through tools and documentation
Medium-Term Vision
- Expanding to more languages and dialects
- Domain-specific fine-tuned models for key sectors
- Voice-first applications at population scale
- Global expansion to other multilingual markets
Long-Term Impact
- Proving India can lead in foundational AI
- Building sovereign capability for generations
- Inspiring a new wave of Indian AI researchers and entrepreneurs
- Shaping global AI with Indian perspectives and priorities
Conclusion: A Defining Moment for Indian AI
Sarvam AI’s 105B model is more than a technological achievement—it’s a declaration of India’s arrival as a serious contender in the global AI landscape.
Not by copying what works in Silicon Valley. Not by chasing parameter counts for their own sake. But by building AI that truly understands and serves 1.4 billion Indians—efficient, affordable, multilingual, and culturally aware.
The benchmarks prove Sarvam-105B outperforms larger global models on the tasks that matter for India. The open-source release ensures every Indian developer and startup can build on this foundation. The sovereignty of training on domestic compute ensures strategic independence.
As Pratyush Kumar said: “Sovereign, multilingual, and practical—that’s the Indian way.”
The world now has a new benchmark for what AI should look like when it’s built for the Global South, for linguistic diversity, for population-scale deployment, for real-world constraints.
India isn’t just catching up in AI. With Sarvam, India is showing a different path forward.
