Bytedance is training an AI model with up to ten trillion parameters: That's three times the size of Moonshot's Kimi K3, currently the largest Chinese model.

Having run models locally, RAM use seems to be almost directly proportional to number of parameters. 8 Billion parameters requires approx 8GB of VRAM at 1/4 precision.

Therefore, if this pattern holds you somehow need 10 Terabytes of VRAM at 4K and 40 Terabytes at full precision.

I think I saw some estimates that Claude’s Opus models may be and Opus model equivalents may be at around 100B parameters (100-400GB VRAM).

TLDR its clear why RAM is so expensive.