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- 🤖 Apple's Breakthrough in Running Advanced Models on Memory-Limited Devices
🤖 Apple's Breakthrough in Running Advanced Models on Memory-Limited Devices
NP#148
Neon Pulse Team
December 28, 2023
Good morning and welcome to the latest edition of neonpulse!
Today, we’re talking about researchers from Apple have developed an innovative method to run large language models (LLMs) on devices with limited memory.
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Apple's AI Breakthrough for Devices with Limited Memory
Apple's recent AI research has led to a significant breakthrough in running Large Language Models (LLMs) on devices with limited memory. This development is a game-changer, particularly for devices that cannot accommodate the entire model in their DRAM (Dynamic Random-Access Memory).
The research team at Apple has focused on optimizing the use of flash memory and DRAM to run LLMs efficiently on resource-constrained devices. By understanding the characteristics of flash memory and DRAM, they have developed a method that allows LLMs up to twice the size of the available DRAM to be run, achieving a 4-5x increase in inference speed on CPUs and a 20-25x increase on GPUs compared to traditional loading approaches.
The team employed two principal techniques: “windowing” and “row-column bundling.” The windowing technique strategically reduces data transfer by reusing previously activated neurons, maintaining a sliding window of recent input tokens in memory. This approach minimizes the amount of data loaded from flash memory for each inference query. The row-column bundling technique leverages the sequential data access strengths of flash memory, storing concatenated rows and columns of up-projection and down-projection layers together in flash memory.
Another aspect of this breakthrough involves exploiting the sparsity observed in the FeedForward Network (FFN) layers of LLMs. This allows for the selective loading of non-zero parameters from flash memory, further optimizing the process.
This research sets a precedent for future AI development, emphasizing the importance of considering hardware characteristics in the development of inference-optimized algorithms for advanced language models. It opens the door for more efficient LLM inference on devices with limited memory, making advanced AI capabilities more accessible across a wider range of devices.
Which aspect of Apple's AI research do you find more impactful? |
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