Summary of Moe-infinity: Efficient Moe Inference on Personal Machines with Sparsity-aware Expert Cache, by Leyang Xue et al.
MoE-Infinity: Efficient MoE Inference on Personal Machines with Sparsity-Aware Expert Cache
by Leyang Xue, Yao Fu, Zhan Lu, Luo Mai, Mahesh Marina
First submitted to arxiv on: 25 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Performance (cs.PF)
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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 proposed MoE-Infinity system is an efficient inference framework for personal machines with limited GPU memory. It leverages the sparse activation patterns of experts in masked language models (LLMs) to optimize expert cache management, leading to significant latency improvements over existing methods like vLLM, Ollama, DeepSpeed, and BrainStorm. MoE-Infinity showcases 3.1-16.7x per-token speedups for various LLM tasks using DeepSeek and Mixtral models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MoE-Infinity is a new way to make language models run faster on personal computers with limited memory. The idea is that these models often only use a few “experts” (specialized parts) at a time, which can be stored in a special cache. By carefully managing this cache, MoE-Infinity makes the model run up to 16 times faster than other methods. This could make language models more accessible and useful for people who want to use them on their own computers. |
Keywords
* Artificial intelligence * Inference * Token
