In this video, we explore quantization, a practical optimization technique that reduces the size and inference cost of LLMs by storing weights in fewer bits (e.g., 8-bit or 4-bit).
You'll learn:
Why is quantization necessary in real-world deployments
Types of quantization (post-training, quant-aware)
Trade-offs between model size and accuracy
How quantized models run on edge devices and GPUs
This is a must-watch if you're looking to deploy LLMs efficiently or understand how optimization impacts production engineering.