Unbelievable! Run 70B LLM Inference on a Single 4GB GPU with This NEW Technique

The article discusses techniques for extreme memory optimization of large language models, enabling inference on a single 4GB GPU without sacrificing model performance. The key technique is layer-wise inference, which involves loading only the necessary layer from disk, performing calculations, and freeing the memory afterward. Other techniques include single layer optimization using flash attention, model file sharding, using a virtual device called meta device, and using the open-source library AirLLM.

However, the article notes that while inference can be optimized with layering, training cannot be similarly optimized on a single GPU due to the need for more data. Techniques like gradient checkpointing can help reduce training memory requirements. The article concludes by acknowledging the contributions of the Kaggle community and promising to continue open sourcing the latest methods and advances in AI.

Key takeaways:

The large language model requires a significant amount of GPU memory, but it is possible to run inference on a single 4GB GPU using memory optimization techniques that do not require model compression.
The key techniques for extreme memory optimization of large models include layer-wise inference, single layer optimization (Flash Attention), model file sharding, and the use of a meta device.
The authors have open-sourced a library called AirLLM that allows users to implement these techniques with a few lines of code.
While inference can be optimized with layering, training cannot be similarly optimized on a single GPU due to the need for more data and the process of gradient calculation.

Unbelievable! Run 70B LLM Inference on a Single 4GB GPU with This NEW Technique

Key takeaways:

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