LLM VRAM Calculator

The LLMVRAMCalculator.py provides a tool for estimating the VRAM requirements for running large language models (LLMs) on GPUs. It calculates the sizes of the model, context, and total VRAM required for EXL2 and GGUF quantization.

Kudos to NyxKrage

This wouldn't be possible without the LLM Model VRAM Calculator from NyxKrage, so thank you and all credits to him!

Install

Install using pip:

pip install git+https://github.com/pandora-s-git/LLMVRAMCalculator.git

Example

import json
import LLMVRAMCalculator

exl2 = LLMVRAMCalculator.compute_sizes_exl2("Nexusflow/Starling-LM-7B-beta", 8192, cache_bit = 16, bpw = 4.5)
print(json.dumps(exl2, indent = 4))

print(LLMVRAMCalculator.get_gguf_quants())

gguf = LLMVRAMCalculator.compute_sizes_gguf("Nexusflow/Starling-LM-7B-beta", 8192, quant_size = "Q4_K_S")
print(json.dumps(gguf, indent = 4))

Output:

{
    "params": {
        "hf_model": "Nexusflow/Starling-LM-7B-beta",
        "context": 8192,
        "cache_bit": 16,
        "bpw": 4.5
    },
    "model_size": 3.793727159500122,
    "context_size": 1.5293059349060059,
    "total_size": 5.323033094406128
}
['Q2_K', 'Q3_K_S', 'Q3_K_M', 'Q3_K_L', 'Q4_0', 'Q4_K_S', 'Q4_K_M', 'Q5_0', 'Q5_K_S', 'Q5_K_M', 'Q6_K', 'Q8_0']
{
    "params": {
        "hf_model": "Nexusflow/Starling-LM-7B-beta",
        "context": 8192,
        "quant_size": "Q4_K_S"
    },
    "model_size": 3.8611711978912355,
    "context_size": 1.5293059349060059,
    "total_size": 5.390477132797241
}