GPU Memory

GPU memory limitations pose a significant bottleneck for training and deploying increasingly large language models (LLMs) and other deep learning models. Current research focuses on optimizing memory usage through techniques like key-value cache compression (e.g., using attention weights to prioritize information), activation offloading to faster storage, and innovative memory management strategies such as speculative decoding and dynamic tensor allocation. These advancements are crucial for enabling the efficient training and inference of large models on both high-end and consumer-grade hardware, impacting the accessibility and scalability of AI applications across various domains.