NVIDIA Cuts Rubin GPU Memory and NVLink Bandwidth Specs as First Production Hardware Ships
NVIDIA revised Rubin platform specs before mass production: HBM4 bandwidth down 11%, NVLink down 17%, but scale-out networking improved.
As NVIDIA's next-generation AI computing platform, Vera Rubin, enters mass production, the company has quietly revised the final shipping specifications for its Rubin GPUs and associated systems. According to reports by technology outlet ServeTheHome, the new specs show reductions in memory and interconnect bandwidth compared to preliminary figures announced last year, though scale-out networking bandwidth has seen a slight increase. This shift occurs as hyperscalers begin deploying the first NVL72 systems, indicating that delivered hardware will perform differently from early marketing claims.
Specifically, the HBM4 memory bandwidth per Rubin GPU has dropped from an initial target of 22TB/s to 19.2TB/s, a reduction of approximately 11%. This stems from adjusting the memory stack operating frequency from an expected 10.75Gbps/pin to 9.375Gbps/pin. While this remains higher than competitor AMD's MI455X at 7.6Gbps/pin, the cut will directly impact training and inference workloads reliant on high memory bandwidth. Additionally, NVLink (Scale-Up) bandwidth for multi-GPU interconnects has fallen from 3.6TB/s to 3.0TB/s, a 17% decrease.
To compensate, the Rubin GPU's Scale-Out networking bandwidth has increased from 0.4TB/s to 0.45TB/s. For the rack-scale system Vera Rubin NVL72, total memory bandwidth drops from 1580TB/s to 1400TB/s, and total NVLink bandwidth falls from 260TB/s to 216TB/s, while scale-out networking rises from 28.8TB/s to 32.4TB/s. Notably, the HGX NVL8 system based on Rubin faces a memory capacity cut from 2.3TB to 2.0TB, forcing each GPU to use only 7 HBM4 stacks and resulting in a 26% loss in memory bandwidth.
NVIDIA had previously stated all values were "preliminary and subject to change," making such adjustments common before server hardware launches. However, significantly lowering key bandwidth metrics just before mass production suggests supply chain or technical implementation challenges, possibly related to memory controller speeds, stack yields, or a combination thereof. For customers planning to procure Rubin platforms, this necessitates a re-evaluation of performance expectations for memory-intensive AI models.