Summary of Llmem: Estimating Gpu Memory Usage For Fine-tuning Pre-trained Llms, by Taeho Kim et al.
LLMem: Estimating GPU Memory Usage for Fine-Tuning Pre-Trained LLMs
by Taeho Kim, Yanming Wang, Vatshank Chaturvedi, Lokesh Gupta, Seyeon Kim, Yongin Kwon, Sangtae Ha
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 LLMem is a solution that addresses the challenge of fine-tuning pre-trained large language models (LLMs) with limited hardware resources, particularly GPU memory constraints. Various distributed fine-tuning methods have been proposed to alleviate these constraints, but determining the most effective method for achieving rapid fine-tuning while preventing GPU out-of-memory issues in a given environment remains unclear. LLMem estimates the GPU memory consumption when applying distributed fine-tuning methods across multiple GPUs and identifies the optimal method. Experimental results show that LLMem accurately estimates peak GPU memory usage on a single GPU, with error rates of up to 1.6%. Additionally, it shows an average error rate of 3.0% when applying distributed fine-tuning methods to LLMs with more than a billion parameters on multi-GPU setups. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem with using big language models on computers with limited memory. These models need a lot of processing power and memory, but not all computers can handle it. The researchers developed a way to predict how much memory is needed when training these models on multiple computers at the same time. They tested their method and found that it was very accurate, even for really big models. |
Keywords
» Artificial intelligence » Fine tuning
