Summary of Llmco2: Advancing Accurate Carbon Footprint Prediction For Llm Inferences, by Zhenxiao Fu et al.
LLMCO2: Advancing Accurate Carbon Footprint Prediction for LLM Inferences
by Zhenxiao Fu, Fan Chen, Shan Zhou, Haitong Li, Lei Jiang
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the pressing issue of estimating the carbon footprint of large language models (LLMs) during inference. Unlike training, which has a fixed carbon impact, inference requests vary in size and complexity, making it challenging to estimate their environmental impact. The authors introduce , a graph neural network (GNN)-based model that improves the accuracy of LLM inference carbon footprint predictions compared to existing methods. The GNN model considers various factors, including batch size, prompt length, token generation number, GPU types, and quantities, to provide a more accurate estimate of the carbon impact of LLM inferences. This tool is crucial for users and cloud providers to make informed decisions about their environmental footprint. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a problem with big language models that use lots of energy when people ask them questions. It’s hard to know how much energy these models use because the requests are different each time, like asking a question or writing a short story. The authors created a new tool called that is better at guessing how much energy the model will use than previous tools. This tool takes into account many factors, such as how many questions you ask and what kind of computer it’s running on. This can help people make choices about how to reduce their environmental impact. |
Keywords
» Artificial intelligence » Gnn » Graph neural network » Inference » Prompt » Token
