A new technical paper titled “Challenges and Research Directions for Large Language Model Inference Hardware” was published by Google.
Abstract
“Large Language Model (LLM) inference is hard. The autoregressive Decode phase of the underlying Transformer model makes LLM inference fundamentally different from training. Exacerbated by recent AI trends, the primary challenges are memory and interconnect rather than compute. To address these challenges, we highlight four architecture research opportunities: High Bandwidth Flash for 10X memory capacity with HBM-like bandwidth; Processing-Near-Memory and 3D memory-logic stacking for high memory bandwidth; and low-latency interconnect to speedup communication. While our focus is datacenter AI, we also review their applicability for mobile devices.”
Find the technical paper here. Published Jan 2026.
Ma, Xiaoyu, and David Patterson. “Challenges and Research Directions for Large Language Model Inference Hardware.” arXiv preprint arXiv:2601.05047 (2026).
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