Challenges and Research Directions for Large Language Model Inference Hardware (arxiv.org)

Recent research highlights the significant challenges associated with hardware for Large Language Model (LLM) inference, specifically pointing out that the autoregressive decode phase of Transformer models introduces unique difficulties compared to training. The study emphasizes that memory and interconnect limitations are now more pressing than raw compute power, driven by the increasing demands of recent AI advancements. To tackle these issues, the authors propose four innovative research directions for hardware architecture: firstly, utilizing High Bandwidth Flash to boost memory capacity dramatically while maintaining HBM-like bandwidth; secondly, implementing Processing-Near-Memory and 3D memory-logic stacking to enhance memory bandwidth; and lastly, developing low-latency interconnect solutions that can accelerate communication. While primarily focused on datacenter AI, the research also explores the implications for mobile devices, underscoring the broad relevance of these advancements in optimizing AI performance across different platforms. This exploration of hardware challenges and solutions is crucial as it shapes the future scalability and efficiency of LLM applications in real-world scenarios.