We introduce Shakti, a 2.5 billion parameter language model specifically optimized for resource-constrained environments such as edge devices, including smartphones, wearables, and IoT systems. Shakti combines high-performance NLP with optimized efficiency and precision, making it ideal for real-time AI applications where computational resources and memory are limited. With support for vernacular languages and domain-specific tasks, Shakti excels in industries such as healthcare, finance, and customer service. Benchmark evaluations demonstrate that Shakti performs competitively against larger models while maintaining low latency and on-device efficiency, positioning it as a leading solution for edge AI.
Cite
@article{arxiv.2410.11331,
title = {SHAKTI: A 2.5 Billion Parameter Small Language Model Optimized for Edge AI and Low-Resource Environments},
author = {Syed Abdul Gaffar Shakhadri and Kruthika KR and Rakshit Aralimatti},
journal= {arXiv preprint arXiv:2410.11331},
year = {2025}
}
Comments
Paper in pdf format is 11 pages and contains 4 tables