Ever heard of people running powerful LLMs on their laptop or even a phone?
Or maybe you’ve seen models like DeepSeek or Qwen with names like FP8 or 8-bit attached?
Those aren’t brand-new models, they’re quantized versions. In other words, the same DeepSeek, Qwen, or other open-source LLMs, but optimized through a process called quantization.
So, what exactly is quantization?
What is Quantization?
Quantization is a technique that reduces the precision of a model’s weights and activations.
Most state-of-the-art (SOTA) LLMs use 32-bit (FP32) or 16-bit (FP16) numbers to represent their parameters. With quantization, these can be compressed down to 8-bit or even 4-bit without changing the model’s architecture.
Image from A Visual Guide to Quantization
Think of it like image compression, but for LLMs. Just as lowering an image’s resolution makes the file smaller while still keeping it recognizable, quantization reduces the size of a model by storing its numbers with fewer bits while trying to keep the overall quality intact.
Why Do We Need Quantization?
Modern open-source LLMs are massive:
- DeepSeek V3.1 → 685B parameters
- Kimi-K2-Instruct → 1T parameters
The problem? These huge models demand enormous storage, memory, and compute power, making them almost impossible to run locally on consumer hardware. Larger models also mean slower inference.
Think of it like carrying around a full-size water cooler when all you need is a bottle of water. Both give you water, but one is so big and heavy that it’s impractical for everyday use. Quantization is like pouring that same water into smaller bottles you still get what you need, but it’s lighter, portable, and way easier to handle.
That’s where quantization helps by:
✅ Shrinking model size (less disk storage)
✅ Reducing memory usage (fits in smaller GPUs/CPUs)
✅ Cutting down compute requirements
✅ Speeding up inference
Which makes it possible to:
✅ Run LLMs on personal machines (great for privacy and control)
✅ Deploy efficient models on servers without breaking the budget
✅ Enable startups to fine-tune and host models in-house
The Trade-Off
Of course, there’s no free lunch. Lowering precision comes with some loss in accuracy.
Think of it like saving a photo:
- RAW format (32-bit) keeps every tiny detail.
- JPEG (8-bit) is smaller and still looks good.
- Compress it too much (say 2–4 bits), and it turns into a blurry mess.
A quantized model works the same way.
- A quantized model may not be as “smart” as its original counterpart.
- If you go too low (e.g., 2–4 bits), and the model may start to hallucinate or lose important details.
That’s why it’s important to find the sweet spot: balancing performance gains with acceptable accuracy. For some applications, 8-bit or 4-bit quantization works well without a noticeable drop in quality.
Final Thoughts
Quantization is one of the main reasons people can run LLMs on laptops, edge devices, or even phones. It makes models faster, smaller, and cheaper to operate. But it’s not a magic bullet. Always test your quantized model to see if it still meets your goals whether that’s accuracy, reliability, or user experience.
This post is meant to give a simple overview of quantization and quantized LLMs for readers who aren’t deep into LLM development. If you’d like to dive deeper into the technical side, I’ll share some great resources at the end that explain the math and implementation details.
Check this article for more details about Quantization:
👉 Quantization for Large Language Models (LLMs): Reduce AI Model Sizes Efficiently
👉 A Visual Guide to Quantization
Check this YouTube video by Former Chief Evangelist at Hugging Face:
👉 Deep Dive: Quantizing Large Language Models, part 1
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