Summary of Moe-cap: Benchmarking Cost, Accuracy and Performance Of Sparse Mixture-of-experts Systems, by Yao Fu et al.
MoE-CAP: Benchmarking Cost, Accuracy and Performance of Sparse Mixture-of-Experts Systems
by Yao Fu, Yinsicheng Jiang, Yeqi Huang, Ping Nie, Zhan Lu, Leyang Xue, Congjie He, Man-Kit Sit, Jilong Xue, Li Dong, Ziming Miao, Kai Zou, Edoardo Ponti, Luo Mai
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The Mixture-of-Experts (MoE) architecture has gained popularity in scaling Large Language Models (LLMs), thanks to its sparse activation mechanism that selectively activates a subset of parameters per token, reducing memory bandwidth and compute FLOPs. To further optimize MoE systems, we introduce MoE-CAP, a benchmarking method that evaluates the interaction between model sparsity and hardware heterogeneity across Cost, Accuracy, and Performance (CAP) dimensions. This method integrates cost, performance, and accuracy metrics into a single diagram, providing a more accurate estimation of the impact of sparsity on system performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MoE is a way to make Large Language Models smaller by only using some parts sometimes. This helps computers use less memory and energy. To help people design MoE models that work well with different computers, we created a new tool called MoE-CAP. It shows how different things like cost, speed, and accuracy are affected when you use an MoE model on different computers. |
Keywords
» Artificial intelligence » Mixture of experts » Token
