AI And Memory Wall

Gholami Amir, Yao Zhewei, Kim Sehoon, Hooper Coleman, Mahoney Michael W., Keutzer Kurt. Arxiv 2024

[Paper]    
Applications Efficiency And Optimization Large Scale Training Model Architecture Pretraining Methods Reinforcement Learning Scaling Laws Training Techniques Transformer

The availability of unprecedented unsupervised training data, along with neural scaling laws, has resulted in an unprecedented surge in model size and compute requirements for serving/training LLMs. However, the main performance bottleneck is increasingly shifting to memory bandwidth. Over the past 20 years, peak server hardware FLOPS has been scaling at 3.0x/2yrs, outpacing the growth of DRAM and interconnect bandwidth, which have only scaled at 1.6 and 1.4 times every 2 years, respectively. This disparity has made memory, rather than compute, the primary bottleneck in AI applications, particularly in serving. Here, we analyze encoder and decoder Transformer models and show how memory bandwidth can become the dominant bottleneck for decoder models. We argue for a redesign in model architecture, training, and deployment strategies to overcome this memory limitation.

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