Sakana AI's LLM Optimization Technique Slashes Memory Costs Up To 75%

The technique, called “universal transformer memory,” uses special neural networks to optimize LLMs to keep bits of information that matter and discard redundant details from their context.

Dec 16, 2024

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Researchers at the Tokyo-based startup Sakana AI have developed a new technique that enables language models to use memory more efficiently, helping enterprises cut the costs of building applications on top of large language models (LLMs) and other Transformer-based models.

The responses of Transformer models, the backbone of LLMs, depend on the content …

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