Summary of An Exploration Of the Effect Of Quantisation on Energy Consumption and Inference Time Of Starcoder2, by Pepijn De Reus et al.
An exploration of the effect of quantisation on energy consumption and inference time of StarCoder2
by Pepijn de Reus, Ana Oprescu, Jelle Zuidema
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
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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 This study investigates strategies for reducing energy consumption when inferring Large Language Models (LLMs). Specifically, it explores quantisation and pruning methods, which are used to compress LLMs and decrease their energy demands. The authors use the StarCoder2 framework and find that while quantization can reduce energy usage, it also leads to lower throughput and some accuracy losses. On the other hand, pruning reduces energy consumption but impairs performance. The study highlights the trade-offs between energy efficiency and model accuracy when compressing LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a nutshell, this paper looks at ways to make Large Language Models use less energy while still being useful. Researchers tried two methods – one that makes numbers smaller (quantization) and another that gets rid of some parts that aren’t needed (pruning). They found that quantization can save energy but also makes the model work a bit slower and not as accurately. Pruning saves even more energy, but it also makes the model less good at doing its job. The study shows that finding the right balance between energy efficiency and how well the model works is important. |
Keywords
» Artificial intelligence » Pruning » Quantization
