Friends, welcome back to ARCTutorials. In this episode of my AI Masterclass series, I make the concept of quantization simple: it’s basically reducing the model size while keeping performance as close as possible. A lot of people think they need an H100 to run world-class AI, but the real trick is converting high-precision numbers into smaller, more efficient formats. The result is less memory usage, faster responses, and usually a slight accuracy trade-off—think of it like compression for model weights. I also explain it with our intern analogy: imagine your intern is packing notes and keeps only what’s essential, drops minor details, but still answers most questions accurately. That’s quantization—slimming down the intern’s backpack. From a developer point of view, it’s like taking 32-bit weights and converting them to 16-bit or 8-bit, so your AI model uses less GPU/CPU and your hardware and compute costs don’t blow up. The key takeaway: quantization helps you deploy practical AI on limited hardware (even your local laptop), while remembering you’re reducing weight precision—not removing governance concepts like guardrails or system prompts.