In this video, I benchmark Mistral-7B-Instruct-v0.2 on an NVIDIA H200 DigitalOcean GPU in three formats: FP16, INT8, and 4-bit AWQ — and test how precision impacts reasoning quality, speed, VRAM usage, and real serving density.
We’ll cover:
👉 What quantization actually does to model weights
👉 Where reasoning starts breaking down (FP16 → INT8 → 4-bit)
👉 Why memory savings don’t always reduce total GPU usage in vLLM
👉 Tokens/sec vs aggregate throughput
👉 When 4-bit wins — and when it doesn’t
If you're building AI systems and deciding between full precision and aggressive quantization, this is a practical infrastructure-level breakdown of the real tradeoffs.
Chapters:
00:00 Introduction
00:41 Understanding how quantization works
01:42 Why do you even need quantization
02:38 The experiment we ran
03:56 The observations we had
05:43 Overall learnings
