Anonymous
7/11/2025, 6:21:09 PM
No.105871323
>>105871304
To determine the memory required to run a 1 trillion (1T) parameter LLM model with IQ2_XS quantization, we need to know the bits per parameter (bpw) for IQ2_XS.
According to search results, IQ2_XS quantization uses approximately 2.31 bits per parameter (bpw).
Now, let's calculate the memory:
Total parameters: 1 Trillion = 1×1012 parameters
Bits per parameter (IQ2_XS): 2.31 bpw
Calculate total bits: 1×1012 parameters×2.31 bits/parameter=2.31×1012 bits
Convert bits to bytes: There are 8 bits in a byte.
2.31×1012 bits/8 bits/byte=0.28875×1012 bytes
Convert bytes to gigabytes (GB): 1 GB=10243 bytes≈109 bytes.
0.28875×1012 bytes/(10243 bytes/GB)≈0.28875×1012/1.07374×109 GB
≈268.9 GB
Therefore, a 1 trillion parameter LLM model quantized with IQ2_XS would require approximately 268.9 GB of memory to run.
It's important to note that this calculation is for the model weights only. Additional memory will be needed for:
KV Cache (Key-Value Cache): This stores intermediate activation values during inference and can significantly add to memory requirements, especially for longer context windows.
Activations: While typically smaller than weights for inference, they still consume some memory.
Overhead: There's always some system and framework overhead. Some sources suggest adding a 20% overhead to the base memory calculation. If we apply that:
268.9 GB×1.20≈322.7 GB
So, while the raw weights might be around 268.9 GB, the practical memory requirement for running inference could be closer to 300-350 GB due to these additional factors.
To determine the memory required to run a 1 trillion (1T) parameter LLM model with IQ2_XS quantization, we need to know the bits per parameter (bpw) for IQ2_XS.
According to search results, IQ2_XS quantization uses approximately 2.31 bits per parameter (bpw).
Now, let's calculate the memory:
Total parameters: 1 Trillion = 1×1012 parameters
Bits per parameter (IQ2_XS): 2.31 bpw
Calculate total bits: 1×1012 parameters×2.31 bits/parameter=2.31×1012 bits
Convert bits to bytes: There are 8 bits in a byte.
2.31×1012 bits/8 bits/byte=0.28875×1012 bytes
Convert bytes to gigabytes (GB): 1 GB=10243 bytes≈109 bytes.
0.28875×1012 bytes/(10243 bytes/GB)≈0.28875×1012/1.07374×109 GB
≈268.9 GB
Therefore, a 1 trillion parameter LLM model quantized with IQ2_XS would require approximately 268.9 GB of memory to run.
It's important to note that this calculation is for the model weights only. Additional memory will be needed for:
KV Cache (Key-Value Cache): This stores intermediate activation values during inference and can significantly add to memory requirements, especially for longer context windows.
Activations: While typically smaller than weights for inference, they still consume some memory.
Overhead: There's always some system and framework overhead. Some sources suggest adding a 20% overhead to the base memory calculation. If we apply that:
268.9 GB×1.20≈322.7 GB
So, while the raw weights might be around 268.9 GB, the practical memory requirement for running inference could be closer to 300-350 GB due to these additional factors.