AI BriefingGaLore Enables LLM Training on Consumer GPUs
·2024.03.20 09:00
Key point
With GaLore technology, even 7B-scale LLMs can be efficiently trained on consumer GPUs.
Details
GaLore (Gradient Low-Rank Projection) is a technology that maximizes memory efficiency when training large language models (LLMs).
To solve the high memory footprint problem of the existing Adam optimizer, it projects gradients into a low-rank subspace for storage. Through this, memory required for storing optimizer states can be reduced by over 82.5%.
The key features are as follows:
- Consumer-grade hardware support: Training of Llama-based 7B models is possible even on GPUs such as the RTX 4090.
- Dynamic Subspace Switching: By dynamically switching subspaces to match the changing low-rank structure of gradients during training, full-parameter training capability is maintained.
- Combination with 8-bit optimizers: It can be used together with 8-bit precision optimizers to further maximize memory efficiency, and integration with the bitsandbytes library is currently being researched.
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