NVDA

MRVL

<p>Recently, an article titled "Challenges and Research Directions for Large Language Model Inference Hardware" co-authored by Xiaoyu Ma and David Patterson discussed the challenges and solutions for inference chips of large language models (LLMs). The article pointed out that the main challenges faced by LLM inference lie in memory and interconnects, rather than computational power, and proposed four architectural research directions: high-bandwidth flash memory, near-memory processing, 3D memory logic stacking, and low-latency interconnects. It is expected that in the next 5-8 years, annual sales of inference chips will grow 4-6 times</p>

- The article discusses significant challenges in large language model (LLM) inference hardware and proposes four research directions to address these issues, focusing on memory and interconnect limitations rather than computational power.  
- The authors emphasize the impact of factors like memory bandwidth, low-latency interconnections, and the implications of various model architectures like mixture of experts (MoE) on the feasibility and cost of inference operations.  
- The paper calls for innovations such as high bandwidth flash memory, processing-near-memory technologies, and low-latency interconnects to enhance LLM performance and meet growing industry demands.

Four Solutions for Inference Chips, Written by David Patterson