Summary of Superpipeline: a Universal Approach For Reducing Gpu Memory Usage in Large Models, by Reza Abbasi et al.
Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models
by Reza Abbasi, Sernam Lim
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper introduces Superpipeline, a new framework designed to optimize the execution of large AI models on constrained hardware during both training and inference. The approach dynamically manages model execution by dividing models into individual layers and efficiently transferring these layers between GPU and CPU memory. This reduces GPU memory usage by up to 60% while maintaining model accuracy and acceptable processing speeds. Superpipeline can be applied to large language models (LLMs), vision-language models (VLMs), and vision-based models, making it useful for researchers and professionals working with advanced AI models on limited hardware. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Superpipeline is a new way to make big artificial intelligence (AI) models work better on computers with limited power. Right now, these big models take up too much memory and slow down computers, which makes it hard to use them. Superpipeline solves this problem by breaking the model into smaller parts and moving them around between computer chips. This helps free up memory and make the computer faster. It works with different types of AI models, like ones that understand language or see pictures. This means researchers can use even bigger and better models on their computers, which could help speed up lots of different applications. |
Keywords
» Artificial intelligence » Inference
