In the grand narratives of the Ramayana and Mahabharata, the divine occasionally adopts a Virata form, showcasing immense power and grandeur. However, the divine usually remains in a more accessible, normal form, allowing humans to visualize and worship with ease. This duality serves as an excellent analogy for understanding LLM (Large Language Model) quantization.

Much like the divine's Virata form, full-precision LLMs are vast and powerful, often represented with 32-bit precision for maximum accuracy and performance. However, deploying these full-precision models on resource-constrained devices like phones or IoT devices, which have limited power and memory, is impractical.

To address this, we employ quantization—a process akin to the divine adopting a more approachable form. Quantization reduces the precision of the numbers used to represent a model's parameters, typically from 32-bit to 16-bit or even 8-bit. This reduction in precision significantly lowers the model's computational and memory requirements, making it feasible to deploy on devices with limited resources.

Thus, just as the divine remains accessible to humanity by adopting a form that can be easily visualized and worshiped, quantized LLMs become accessible for deployment on smaller, less powerful devices, ensuring their utility across a broader range of applications.

Lord Krishna showing Vishvarupa to Arjuna

Thanks
Sreeni Ramadurai