Summary of Towards Greener Llms: Bringing Energy-efficiency to the Forefront Of Llm Inference, by Jovan Stojkovic et al.
Towards Greener LLMs: Bringing Energy-Efficiency to the Forefront of LLM Inference
by Jovan Stojkovic, Esha Choukse, Chaojie Zhang, Inigo Goiri, Josep Torrellas
First submitted to arxiv on: 29 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Hardware Architecture (cs.AR); Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to optimize energy efficiency in large language model (LLM) inference serving is presented in this paper. The authors investigate the trade-offs between performance, throughput, and energy consumption when making energy efficiency the primary goal under service-level agreements (SLOs). They demonstrate that various knobs are available to LLM inference providers to achieve energy efficiency, depending on inputs, models, and SLOs. The study characterizes the impact of these knobs on latency, throughput, and energy usage, offering valuable insights into optimizing energy consumption without compromising performance. This work paves the way for sustainable and cost-effective LLM deployment in data center environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super smart computers that can understand and generate human-like text. They’re used all over the place, but they need a lot of power to run. That’s a problem because it takes a lot of energy to keep these computers running. In this paper, some smart people figured out how to make these computers use less energy without sacrificing their ability to do cool things like chat or write stories. |
Keywords
» Artificial intelligence » Inference » Large language model
