Challenges and Research Directions for Large Language Model Inference Hardware

2026-01-08

arXiv.org

AI Summary

This work systematically identifies that the performance bottleneck in large language model (LLM) inference stems primarily from memory bandwidth and interconnect latency rather than computational capacity, a limitation especially pronounced during autoregressive decoding. To address this challenge, the study proposes four hardware architecture innovations tailored for data centers yet also applicable to mobile scenarios: high-bandwidth flash storage, processing-near-memory (PNM), 3D memory-logic stacking, and low-latency interconnects. Together, these directions form a coherent technical roadmap that offers critical guidance for the design of next-generation AI accelerators, emphasizing memory-centric solutions to overcome the constraints of current systems.

Technology Category

• Machine Learning: Hardware-aware ML
• Natural Language Processing: (Large) Language Models
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Large language models for search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Large Language Model
• Inference
• Memory Bottleneck
• Interconnect
• Hardware

Innovation

• High Bandwidth Flash
• Processing-Near-Memory
• 3D memory-logic stacking
• low-latency interconnect
• LLM inference