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The model size is the number of parameters in the Large Language Model (LLM).

An LLM has 70B parameters, it means that the model has 70 billion adjustable parameters.

1. Model Size and Memory Usage

The total memory usage of the model:
memory usage (in bytes) = number of parameters $\times$ size of each parameter

Number of parameters	1 B
32-bit precision	1 $\times$ 1,000,000,000 $\times$ 4 byte = 4,000,000,000 / 1024 / 1024 / 1024 = 3.7253 GByte
16-bit precision	1 $\times$ 1,000,000,000 $\times$ 2 byte = 2,000,000,000 / 1024 / 1024 / 1024 = 1.862645 GByte
8-bit precision	1 $\times$ 1,000,000,000 $\times$ 1 byte = 1,000,000,000 / 1024 / 1024 / 1024 = 0.93132 GByte

Number of parameters	3 B
32-bit precision	3 $\times$ 1,000,000,000 $\times$ 4 byte = 12,000,000,000 / 1024 / 1024 / 1024 = 11.17587 GByte
16-bit precision	3 $\times$ 1,000,000,000 $\times$ 2 byte = 6,000,000,000 / 1024 / 1024 / 1024 = 5.58794 GByte
8-bit precision	3 $\times$ 1,000,000,000 $\times$ 1 byte = 3,000,000,000 / 1024 / 1024 / 1024 = 2.7939677 GByte

Number of parameters	7 B
32-bit precision	7 $\times$ 1,000,000,000 $\times$ 4 byte = 28,000,000,000 / 1024 / 1024 / 1024 = 26.077 GByte
16-bit precision	7 $\times$ 1,000,000,000 $\times$ 2 byte = 14,000,000,000 / 1024 / 1024 / 1024 = 13.038516 GByte
8-bit precision	7 $\times$ 1,000,000,000 $\times$ 1 byte = 7,000,000,000 / 1024 / 1024 / 1024 = 6.519258 GByte

…

thousand [ˈθaʊznd]

1. the number 1,000 (千)
2. a large number

million ['mɪljən]

1. the number 1,000,000 (百万)
2. a large number

billion ['bɪljən]

1. the number 1,000,000,000 (十亿)
2. a very large number

trillion ['trɪljən]

1. the number 1,000,000,000,000 (万亿)

2. Hugging Face

2.1. GPT-Neo 2.7B

https://huggingface.co/EleutherAI/gpt-neo-2.7B/blob/main/README.md
https://huggingface.co/EleutherAI/gpt-neo-1.3B/blob/main/README.md

GPT-Neo refers to the class of models, while 2.7B represents the number of parameters of this particular pre-trained model.

GPT-Neo refers to the class of models, while 1.3B represents the number of parameters of this particular pre-trained model.

2.2. Massive Text Embedding Benchmark (MTEB) Leaderboard

https://huggingface.co/spaces/mteb/leaderboard

Massive Text Embedding Benchmark
https://huggingface.co/mteb

Model Size (million parameters, in number of parameters)
Memory Usage (GB, fp32)

2.3. Open LLM Leaderboard

https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard

the number of parameters (B)
#Params (B)

2.4. LLM-Perf Leaderboard

https://huggingface.co/spaces/optimum/llm-perf-leaderboard

Memory (MB)
Params (B)

2.5. Open Ko-LLM Leaderboard

https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard

#Params (B)

References

[1] Yongqiang Cheng, https://yongqiang.blog.csdn.net/
[2] The Big Benchmarks Collection, https://huggingface.co/collections/open-llm-leaderboard/the-big-benchmarks-collection-64faca6335a7fc7d4ffe974a