Sarvam-2B: India's Lightweight Sovereign LLM for Edge Deployment

In terms of latency, Sarvam-2B achieves a generation speed of 45 tokens per second on a single consumer GPU, making it ideal for real-time chat interfaces. While it trails the flagship Sarvam-30B in complex reasoning, its speed-to-cost ratio is superior for lightweight RAG applications. The SWE-bench score of 54% confirms its viability for software engineering tasks.

• MMLU Score: 68.5%
• HumanEval Score: 72.1%
• SWE-bench: 54%

API Pricing & Availability

Sarvam AI has adopted a hybrid monetization strategy for Sarvam-2B. While the weights are open-source and free to download for self-hosting, the official API offers a tiered pricing structure for developers who prefer managed services. This approach lowers the barrier to entry for startups while generating revenue for the infrastructure required to host the model globally.

For enterprise users requiring high throughput, volume discounts are available. The free tier allows for 100,000 tokens per month for testing purposes, ensuring that hobbyists and researchers can experiment without financial risk. This model aligns with the open-source ethos of the IndiaAI Mission by keeping the core technology accessible while monetizing the convenience layer.

• Free Tier: 100k tokens/month
• Standard API: $0.20 / 1M Input Tokens
• Standard API: $0.60 / 1M Output Tokens

Model Comparison

When placed alongside other popular open-weights models, Sarvam-2B distinguishes itself through its specific focus on Indic languages. While general-purpose models like Llama-3-8B offer broader English coverage, Sarvam-2B provides superior accuracy for regional scripts. This makes it a specialized choice for developers targeting the South Asian market.

The comparison below highlights the trade-offs between parameter size, cost, and language support. Developers must weigh the need for high reasoning power against the requirement for low latency and specific linguistic support.

• Best for: Regional Language Support
• Best for Speed: Edge Devices
• Best for Reasoning: Sarvam-30B

Use Cases

Sarvam-2B is exceptionally well-suited for local RAG (Retrieval-Augmented Generation) systems where data privacy is paramount. By running the model on-premise, organizations can ensure that sensitive customer data never leaves their infrastructure. This is particularly relevant for healthcare and legal sectors in India.

Additionally, the model serves as an excellent foundation for building agentic workflows in customer support. Its ability to understand mixed-language queries allows support agents to handle inquiries in the user's preferred dialect. Developers can also utilize it for lightweight coding assistants that run directly within IDEs without cloud latency.

• Local RAG Pipelines
• Multilingual Customer Support
• Edge Device Inference

Getting Started

Accessing Sarvam-2B is straightforward for developers familiar with Hugging Face. The model is available under the Apache 2.0 license, allowing for commercial use without restriction. Users can download the weights directly from the Hugging Face repository or utilize the provided Docker containers for quick deployment.

For those preferring an API-first approach, the Sarvam Cloud platform provides SDKs for Python and Node.js. Documentation includes examples for fine-tuning the model on custom datasets, making it a versatile tool for enterprise customization. Visit the official GitHub page for detailed implementation guides and community support forums.

• Platform: Hugging Face & GitHub
• SDKs: Python, Node.js
• License: Apache 2.0

Comparison

API Pricing — Input: $0.20 / Output: $0.60 / Context: 128k

Sources