Apple researchers have developed an innovative approach to efficiently run large language models (LLMs) on devices with limited memory. Their method involves storing LLM parameters on flash memory and selectively transferring data to DRAM as needed, resulting in significant improvements in inference speed and I/O latency. The study emphasizes the importance of considering hardware characteristics in optimizing algorithms for advanced language models.
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AI Research: Running Large Language Models on Devices with Limited Memory
Researchers at Apple have developed an innovative method to efficiently run large language models (LLMs) on devices with limited DRAM capacity. This breakthrough addresses the challenges posed by intensive computational and memory requirements.
Key Techniques and Benefits
The research introduces a strategy involving storing LLM parameters on flash memory and dynamically bringing them to DRAM as needed during inference. It focuses on optimizing data transfer and memory utilization. The two principal techniques, “windowing” and “row-column bundling,” strategically reduce data transfer, increase data chunk size, and leverage sparsity in FeedForward Network (FFN) layers, leading to remarkable increases in inference speed.
Practical Value and Applications
This approach paves the way for more efficient LLM inference on devices with limited memory, crucial for overcoming computational bottlenecks. It offers a promising solution to the challenges associated with running large models on constrained devices, emphasizing the importance of considering hardware characteristics in the development of inference-optimized algorithms for advanced language models.
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