Challenges and Research Directions for Large Language Model Inference Hardware

• URL: http://arxiv.org/abs/2601.05047v2
• Date: Wed, 14 Jan 2026 20:37:46 GMT
• Title: Challenges and Research Directions for Large Language Model Inference Hardware
• Authors: Xiaoyu Ma, David Patterson,
• Abstract summary: Exacerbated by recent AI trends, the primary challenges are memory and interconnect rather than compute.<n>High Bandwidth for 10X memory capacity with Flash-like bandwidth; Processing-Near-Memory and 3D memory-logic stacking for high memory bandwidth.<n>Low-latency interconnect to speedup communication.
• Score: 7.216091397339619
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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