Quantization is a technique used to reduce the memory footprint and improve inference speed of large language models (LLMs) by representing weights with lower precision (e.g., 8-bit integers instead of 16-bit floating point numbers).
In this project, we successfully quantized TinyLlama-1.1B-Chat from FP16 (16-bit floating point) to 8-bit using the transformers library and bitsandbytes.
This guide explains:
Why quantization is important
How to quantize TinyLlama to 8-bit
How to save and reuse the quantized model
How to evaluate performance (loss & perplexity)
Why this approach is useful for others
Quantization provides several key benefits:
-
Memory Efficiency
16-bit models require more VRAM/RAM. Converting to 8-bit halves the memory requirements, allowing larger models to fit on smaller GPUs. -
Faster Inference
8-bit models often have faster inference since they load fewer bytes per weight. -
Accessibility
People with lower-end GPUs (e.g., 4GB/6GB VRAM) can run models that otherwise wouldn’t fit. -
Cost Efficiency
Lower memory usage = cheaper cloud instances.
Tradeoff: Quantization introduces tiny precision loss, but for most inference/chat use cases, the difference is negligible.
Make sure you have Python 3.9+ and install:
pip install torch transformers bitsandbytes accelerate