Why can SRAM outperform GPU in AI reasoning scenario?

In the AI power circuit,traditional GPU has occupied the mainstream of the market for a long time by virtue of HBM's high bandwidth advantage.Even though HBM's bandwidth has been iterated to tens of TB/s,SRAM architecture chips still show significant performance advantages and competitiveness in large model reasoning scenarios.The core of the performance differentiation between the two is that there are essential differences in computing logic and operating mechanism in the two stages of AI training and reasoning,which also enables SRAM to successfully break through the performance limitations of traditional computing architecture.
 

AI model training is a typical computing-intensive scenario,and it is also the core advantage of GPU.In the training process,after the model weights are loaded into the chip,it is necessary to perform repeated matrix operations with massive training data.In the reasoning scenario with extremely high computational strength and autoregressive decoding of large language models,the running logic is completely reversed,from the computational power limited mode to the memory access bandwidth limited mode,which is also the core advantage scenario of SRAM architecture.In model reasoning,every time a Token is generated,the full model weight needs to be read completely,and the chip computing core is idle and waiting for most of the time,and the"memory wall"and bandwidth bottleneck problems commonly faced by the industry are infinitely magnified.
 

The traditional GPU adopts the off-chip HBM streaming loading mode,and the model weights are stored off-chip,and the data needs to be retrieved repeatedly across chips for each calculation.Due to the inherent limitations of chip physical structure and pin routing,the off-chip interface bandwidth has a physical ceiling that cannot be broken.In contrast,SRAM architecture adopts on-chip resident memory design,which can permanently retain the full model weight in on-chip SRAM,completely avoiding the frequent data handling loss on and off-chip.
 

Relying on the unique architectural advantages,SRAM gets rid of the physical constraints of external interfaces,and its internal storage bandwidth can reach 100-150TB/s stably,which is several times to dozens of times of the off-chip bandwidth of traditional GPU.This breakthrough greatly reduces the data reading delay,reduces the cold start delay of AI reasoning to sub-millisecond level,and fully releases the computing power potential of the chip computing core,which has become the preferred technical solution for efficient AI reasoning.