Big businesses are no longer the only ones using artificial intelligence. Startups, small companies, and individual developers can now create effective AI solutions without breaking the bank thanks to small language models. Particularly in India, where there are limited resources, a wide variety of languages, and uneven internet connectivity, SLMs provide something that big AI models just cannot: usefulness. If you're an Indian company trying to get started with AI in a sensible way, this article explains what SLMs are, how they compare to larger models, and which ones are worth your attention.
A Small Language Model: What Is It?
Generative AI models known as Small Language Models, or SLMs, are capable of processing, comprehending, and producing text, audio, video, and code. SLMs have a limited scope and scale.
Only a few million to a few billion parameters are used in their training. The AI model learns how to function, evaluate a query, and provide a suitable output using these parameters. For SLMs, the magnitude of the parameters is enormous, but it is much smaller than for LLMs. Giving users access to the same AI technology without its vast size and computational expenses is the main goal of employing SLMs.
Here are a few instances of little language models:
Llama 3.2-1B: This 1-billion-parameter model, created by Meta, is intended for edge devices with constrained processing power, such as laptops and phones.
Alibaba's 1.5 billion-parameter model, Qwen 2.5-1.5B, was created with robust multilingual support.
DeepSeek-R1-1.5B: Distilled from the bigger Qwen 2.5 model, DeepSeek's reasoning-focused model with 1.5 billion parameters retains strong logical thinking in a smaller size.
Hugging Face created the 1.7 billion-parameter SmolLM2-1.7B model, which is trained on carefully chosen public datasets to outperform its size.
Phi-3.5-Mini-3.8B: Despite its small size, Microsoft's compact 3.8-billion-parameter model is optimised for reasoning and producing code.
Gemma 3-4B: A 4-billion-parameter model from Google DeepMind that can comprehend both text and visuals and is multilingual.
Because Small Language Models run on common devices without requiring expensive computing power, they enable AI to be used by a greater number of people.
What distinguishes it from a Large Language Model?
The first generative AI models were called Large Language Models, or LLMs. These are capable of performing the same functions as SLMs. However, compared to tiny language models, they employ a lot more parameters. To train itself, an LLM typically employs hundreds of billions to trillions of parameters. Despite having significantly more generating power than an SLM, LLMs are challenging to deploy on small edge devices because to their size and compute cost.
Large data centers and a lot of water are also needed for LLM cooling. By guaranteeing that the model operates locally on edge devices without requiring the internet or cloud providers, small language models can assist in resolving this issue. Thus, Small Language Models were created to make AI more widely available.
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