this post was submitted on 07 Feb 2024
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I'm afraid to even ask for the minimum specs on this thing, open source models have gotten so big lately
Every billion parameters needs about 2 GB of VRAM - if using bfloat16 representation. 16 bits per parameter, 8 bits per byte -> 2 bytes per parameter.
1 billion parameters ~ 2 Billion bytes ~ 2 GB.
From the name, this model has 72 Billion parameters, so ~144 GB of VRAM
Llama 2 70B with 8b quantization takes around 80GB VRAM if I remember correctly. I’ve tested it a while ago.