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this post was submitted on 07 Aug 2026
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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.
for inference you're only counting active parameters towards VRAM, and some labs / models don't train at 32b precision, or even use the same precision for different parts of the network