Google has released a technical paper titled “Challenges and Research Directions for Large Language Model Inference Hardware.” This paper addresses the complexities of Large Language Model (LLM) inference, particularly the autoregressive decode phase of Transformer models, which differs fundamentally from training processes. The authors highlight that current challenges in LLM inference focus primarily on memory and interconnect issues rather than computational power. To tackle these limitations, they propose four innovative architectural research directions: utilizing High Bandwidth Flash to enhance memory capacity, implementing Processing-Near-Memory techniques, exploring 3D memory-logic stacking for increased bandwidth, and developing low-latency interconnects to improve communication speed. While the research primarily targets datacenter AI applications, the approaches also have potential for mobile devices. For further details, access the technical paper published in January 2026 by Ma, Xiaoyu, and David Patterson on arXiv.
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