Day 22/30: Quantization Explained 🤯 (How 40GB AI Models Run on Phones) #AI #LLM #30daysai #tech

Añadido: 2026-05-25

[00:00:00]This AI model was 40 GB.

[00:00:03]Now it runs on a phone. Modern AI models contain billions of parameters and each parameter stores numerical values usually using large floatingpoint precision.

[00:00:17]That means huge GPU memory usage. Most models use FP32 precision.

[00:00:25]That means 32 bits per number.

[00:00:29]very accurate but extremely memory expensive.

[00:00:34]Quantization reduces numerical precision like converting FP32 to int8 smaller numbers.

[00:00:45]Same model, less memory. A 40 GBTE model can become 10 GB smaller, faster, and dramatically cheaper to run. Smaller models transfer less memory, use CPUs and GPUs more efficiently, and perform faster matrix operations.

[00:01:09]That reduces inference cost and improves deployment speed. But there's a trade-off.

[00:01:16]You lose a small amount of accuracy in exchange for massive efficiency gains. Quantization powers phone AI, edge AI devices, local LLMs, fast inference APIs and tools like llama.cpp.

[00:01:37]AI progress isn't just smarter models, it's efficient models. Like and subscribe for day 23.