The conversation around GPUs in Gen AI talks often jumps straight to "just rent an H100" without explaining why.
I wrote a visual guide covering the vocabulary that actually matters:
š¹ Why GPUs over CPUs (it's not just "more cores")
š¹ HBM vs GDDR ā why your RTX 4090 can't run Llama 405B
š¹ FLOPs, TFLOPS, and what those spec sheets actually mean
š¹ Precision formats: FP32 ā FP16 ā BF16 ā FP8
š¹ The memory formula: Parameters Ć Bytes = VRAM needed
š¹ How inference actually works ā from prompt to prediction
š¹ Temperature: the inference-time knob everyone uses but few explain
This isn't about which GPU to buy.
It's about building the mental model so you can read a spec sheet, estimate memory requirements, and have informed conversations about infrastructure.
Part 1 of a 3-part series - https://medium.com/@vinodh.thiagarajan/the-vocabulary-of-gpus-for-ml-budding-gen-ai-engineers-7a693b53b74b

Top comments (0)