IndicServeBench: A Streaming Inference Benchmark Tool for Indian Language Large Models

IndicServeBench is a streaming inference benchmark tool for Indian language large language models (LLMs), supporting three language variants: Hindi, Tamil, and Hinglish (Hindi-English mixed), filling the gap in standardized performance evaluation for Indian language LLMs. Maintained by aryansri05, the project was released on GitHub on May 25, 2026 (link: https://github.com/aryansri05/indicservebench), providing a systematic evaluation solution for Indian language LLMs.

Current AI benchmarks are English-centric and have limited coverage of Indian languages. Indian languages have unique features such as complex writing systems (e.g., Devanagari, Tamil script), rich morphological variations, and code-mixing (e.g., Hinglish). Existing tools struggle to meet their systematic evaluation needs, leading to a lack of unified metrics for the performance of Indian language LLMs.

IndicServeBench focuses on streaming inference testing, which differs from traditional batch inference. Streaming inference returns results step by step, where first-token latency and transmission performance are key metrics directly affecting the user experience of interactive AI systems. This tool simulates real streaming scenarios, helping developers understand the model's performance in actual interactive environments and providing important reference value for application selection.

The project covers three key language variants:

1. Hindi: A widely used official language in India, written in Devanagari script, with complex morphology and grammar, serving a large number of users in northern and central India;
2. Tamil: The official language of Tamil Nadu in southern India, a classical language with a long history, using the unique Tamil script;
3. Hinglish: A code-mixed form of Hindi and English, commonly used in daily communication, posing special challenges to the model's understanding and generation capabilities.

The value of standardized benchmark testing includes:

• Providing objective metrics to support performance comparison of different models, helping developers select appropriate models;
• Encouraging researchers to optimize models, especially promoting technological progress in Indian language communities with fewer resources;
• Systematic testing reveals model weaknesses and biases, providing directions for improvement.

Applicable groups:

• Model developers: Verify the performance of Indian language models and identify areas for improvement;
• Application developers: Evaluate and compare models to provide data support for product selection;
• Research community: A standardized evaluation platform to ensure the comparability of research results;
• Fairness organizations: Monitor the performance of Indian language AI to ensure that technology serves all language communities fairly.

Unique challenges faced:

• Text processing: Differences in character sets and typesetting rules across multiple writing systems;
• Resource limitations: Relatively scarce digital resources and annotated data for Indian languages;
• Code mixing: Mixed languages like Hinglish have no fixed grammar, with flexible mixing of vocabulary and grammar, testing the model's understanding ability;
• Metric adaptation: Need to adjust evaluation metrics based on the characteristics of Indian languages, avoiding direct transplantation of English benchmark methods.

IndicServeBench is an important step in the diversified and inclusive development of AI, ensuring that the Indian language community is not overlooked in the development of LLMs. We look forward to community participation in improving the project, promoting the performance of Indian language models and the widespread application of AI among Indian users. At the same time, it provides a localized benchmark example for the global AI community, helping to popularize AI technology worldwide.