The Rise of Small Language Models (SLMs): The Next Big Shift in India
- Rajesh Kalyan
- Sep 10
- 5 min read
Artificial intelligence (AI) has been one of the most disruptive forces of the 21st century. From transforming industries to reshaping everyday experiences, AI continues to evolve in ways that redefine what’s possible. For years, the spotlight has been firmly on Large Language Models (LLMs) like GPT, Gemini, and Claude, which stunned the world with their capacity to generate human-like text, answer complex questions, and even produce creative content.
But as we enter 2025, a new chapter in AI innovation is emerging, one driven not by size, but by efficiency and relevance. The rise of Small Language Models (SLMs) marks a decisive shift, especially in India, where scalability, affordability, and accessibility are key. These models promise to bring AI closer to the people and industries that need it most, without the massive infrastructure overhead associated with LLMs.
This blog explores what SLMs are, why they matter, and how they are set to transform industries like healthcare, finance, automotive, and retail in India.
What Are Small Language Models (SLMs)?
At their core, Small Language Models are compact, resource-efficient versions of LLMs. While they share the ability to generate natural, human-like text, they are trained on smaller datasets and designed with domain-specific precision in mind. Unlike their heavyweight counterparts, SLMs are:
Easier to train and deploy - they can run effectively on modest infrastructure.
Resource-efficient - consuming significantly less GPU/CPU power.
Cost-effective - especially for businesses with targeted or niche AI requirements.
More secure and adaptable - since they can be trained on proprietary, private datasets.
In essence, SLMs democratise AI by making it accessible to smaller businesses, startups, and organisations that lack the resources to implement LLMs.
Why SLMs Are Gaining Ground
The push for smaller, smarter models is driven by two realities: cost and relevance.
By 2025, Microsoft’s Phi and Google’s Gemma emerged as frontrunners in lightweight AI models, showing that accuracy doesn’t always require scale. These SLMs demonstrated not only high performance but also lower hallucination rates, addressing one of the biggest concerns with LLMs.
This is particularly critical for industries like healthcare and finance, where precision, compliance, and privacy are non-negotiable. India, with its diverse needs and growing digital infrastructure, is uniquely positioned to benefit from these models.
Sectoral Impact of SLMs
1. Healthcare: Bridging Gaps in Patient Care
India’s healthcare system faces two major challenges: accessibility and workforce shortages. With a doctor-patient ratio below the WHO recommendation, AI can step in as an assistant rather than a replacement.
SLMs in healthcare can:
Streamline Electronic Health Records (EHRs): Automating the summarisation of medical histories and transcribing doctor-patient interactions.
Support preliminary diagnostics: Providing AI-assisted screening in rural or underserved regions, especially for diseases like tuberculosis or diabetes.
Enable patient communication: Conversational agents in regional languages that improve healthcare literacy and access.
2. Finance: Smarter, Safer, Faster
India’s financial sector has embraced digital innovation faster than many others, but fraud, compliance, and customer trust remain key challenges.
SLMs can play a role by:
Personalising financial advice: Offering guidance on investments, insurance, or credit tailored to individual profiles.
Enhancing fraud detection: Analysing transaction patterns in real-time with high accuracy.
Automating document processing: Reducing back-office load by handling KYC forms, loan applications, and compliance paperwork.
3. Automotive: Driving the Next Wave of Innovation
The automotive industry is undergoing a rapid transformation, with electric vehicles (EVs), connected mobility, and autonomous driving reshaping how we think about transport. SLMs bring AI innovation closer to the vehicle and its users.
In-Vehicle Assistance: SLMs can power lightweight, offline voice assistants for navigation, infotainment, and driver support, without requiring constant internet connectivity.
Predictive Maintenance: By analysing sensor data from vehicles, SLMs can predict mechanical failures and recommend proactive servicing, reducing downtime.
Customer Engagement: Automotive companies can use domain-specific SLMs for personalised post-sales support, from service reminders to troubleshooting guides in local languages.
4. Retail: Redefining Customer Experience
Retail in India is driven by diversity, from large e-commerce platforms to mom-and-pop kirana stores. SLMs can enable personalised, hyper-local experiences at scale.
Smart Customer Support: AI chatbots that understand local languages and cultural nuances.
Inventory & Supply Chain Optimisation: Analysing purchasing patterns and predicting demand fluctuations.
Enhanced Loyalty Programs: Generating personalised offers, rewards, and recommendations for customers based on micro-segmentation.
Real-World Applications of SLMs
Beyond industries, SLMs are changing how AI interacts with technology at the edge. Their ability to run in limited-resource environments makes them ideal for India’s diverse infrastructure.
Customer Support: Multilingual, empathetic virtual agents capable of handling diverse queries with precision.
Legal & Manufacturing: Automating document review, contract drafting, and compliance checks while streamlining production workflows.
IoT & Mobile Devices: Bringing generative AI to smartphones, wearables, and connected devices for real-time, personalised user experiences.
The Role of Open-Source and Fine-Tuning
The open-source wave of 2025 unlocked new opportunities for developers and startups. With frameworks like Hugging Face and open weights from major AI labs, fine-tuning SLMs has become more accessible than ever.
Many organisations are now:
Using LLMs as a base and distilling them into smaller, efficient SLMs.
Fine-tuning SLMs on domain-specific datasets for customised use cases.
Prioritising data sovereignty by training SLMs on proprietary data, ensuring compliance with India’s Digital Personal Data Protection Act (DPDPA).
This movement ensures that businesses own their AI journey, instead of depending solely on external, monolithic systems.
Shaping the Future: SLMs in India’s Growth Story
India’s digital economy is projected to surpass $1 trillion by 2030, and AI will be a key driver of this growth. The rise of SLMs makes AI adoption practical, scalable, and inclusive.
Here’s how SLMs are shaping the future across sectors:
Automotive: Enabling connected mobility solutions that work even in low-network areas, supporting EV ecosystems, and enhancing driver safety.
Banking & Finance: Delivering secure, explainable AI that meets compliance while democratising access to financial services.
Healthcare: Bridging rural-urban divides, improving diagnostics, and enhancing patient engagement in local languages.
Retail: Creating hyper-personalised, multilingual shopping experiences for India’s diverse customer base.
The future of AI in India will not be defined by sheer computational might, but by relevance, accessibility, and adaptability — all strengths of Small Language Models.
Conclusion
The rise of Small Language Models signals a profound shift in the AI landscape. They represent a move from “bigger is better” to “smaller is smarter,” making AI more democratic, secure, and cost-efficient.
For India, this shift couldn’t come at a better time. With its vast population, linguistic diversity, and unique challenges across sectors, SLMs provide the right balance of innovation, affordability, and practicality. From enabling rural healthcare to powering connected cars, SLMs are poised to redefine how India adopts and benefits from AI in the coming decade.
In 2025 and beyond, the future of AI in India will be small but mighty. To know more about our solutions: https://www.achalasolutions.com/ Contact us today
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