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Demystifying Quantization: Why It Matters for LLMs and Inference Efficiency

As Large Language Models (LLMs) like GPT, LLaMA, and DeepSeek reach hundreds of billions of parameters, the demand for high-speed, low-cost inference has skyrocketed. Quantization is a technique that helps drastically reduces model size and computational requirements by using lower-precision numbers. In this blog, we will discuss quantization and why it is essential.

Quantization

What Is Quantization?

In deep learning, quantization is the process of converting high-precision floating-point numbers (like FP32 or FP16) into lower-precision formats such as INT8, INT4, or even binary values. Instead of using 32 or 16 bits to represent every weight or activation, quantized models may use as little as 4 or 8 bits per value. This helps reduce memory usage and compute cost significantly.

There are two common types of quantization:

Post-training Quantization (PTQ)

This method quantizes a trained model without re-training the underlying model.

Quantization-Aware Training (QAT)

This method train the model while simulating quantization, which improves accuracy in lower precision.

Why Is Quantization Needed?

As LLMs grow in size, deploying them becomes expensive and resource-intensive. For example,

  • A 175B model in FP16 can take over 350 GB of GPU memory.
  • High-precision inference requires more bandwidth and power.
  • Running large models can require expensive clusters of H100/A100 GPUs.

This is where quantization shines. By reducing precision, models can:

  • Fit into smaller GPU VRAM (e.g., a 65B model in INT4 might fit where only a 13B FP16 model could)
  • Run on less powerful hardware (e.g. older GPUs)
  • Achieve faster inference

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How Does Quantization Help?

✅ Smaller Model Size

Quantized models can shrink by 2x to 8x in size. For example:

  • An FP16 model: 16 bits per weight
  • INT4 model: 4 bits per weight (i.e. 4× smaller)

This means more models per GPU, faster loading times, and lower costs for cloud inference.

⚡ Faster Computation

Lower-precision arithmetic is significantly faster, especially on modern AI accelerators. For example, GPUs like NVIDIA H100 have dedicated Tensor Cores for INT8/FP8. As a result, Quantized inference enables higher token throughput and lower latency.

🔋 Lower Power Consumption

With reduced computation and memory movement, quantized models consume less energy—critical for large-scale inference.

What About Accuracy?

Everything we discussed about quantization so far comes across like "sunshine, rainbows and unicorns". The reality is that there is a trade-off. Reducing precision can run the risk of the following:

  1. Loss of model fidelity
  2. Degraded performance on nuanced tasks

With advanced techniques described above, quantized LLMs can retain 95–99% of original accuracy while gaining massive performance and cost benefits.

Conclusion

In this blog, we learnt about quantization and how it is extremely critical for running LLMs at scale. Quantization has become a standard best practice for deploying and operating production-grade models at scale. It enables:

  • Faster, cheaper inference
  • Reduced memory and energy use
  • Broader hardware compatibility