NVIDIA Plans to Downgrade Rubin Ultra HBM Configuration to Address High-End Shortage, Has Tested at Least Three Low-Memory Versions

BlockBeats News, August 7th - NVIDIA is currently evaluating the adjustment of the next-generation AI GPU Rubin Ultra's HBM configuration. The company plans to launch a version with lower VRAM than originally planned to ease the production pressure caused by the high-end HBM supply shortage. Insiders revealed that NVIDIA has tested at least three different VRAM capacity versions in the past few weeks, some of which use lower memory specifications. This means that even NVIDIA, which dominates the GPU market, must compromise between product specifications and supply capabilities. The Rubin Ultra is positioned higher than the upcoming Rubin series, which was originally designed to be equipped with higher-capacity and higher-bandwidth HBM to improve large model training and inference performance.

If the final configuration adopts lower VRAM, customers running large language models and other large AI workloads will need to deploy more GPUs, leading to increased system costs and cluster complexity. For cloud providers such as Microsoft, Meta, Amazon, and Google, who are continuously expanding their AI capital expenditures, data center construction costs may further rise. NVIDIA's move also reflects that, despite having the strongest bargaining power in the industry chain, HBM supply constraints remain a core bottleneck for AI computing power expansion. Previously, SK Hynix and Micron's high-end HBM capacities have been consistently tight, and NVIDIA's proactive reduction in configuration this time further confirms that the HBM supply-demand contradiction is difficult to alleviate in the short term.