✳flâneur — a map of the web's best reading

NVIDIA Tensor Core Evolution: From Volta To Blackwell

newsletter.semianalysis.com · 5,469 words · saved by 1 readers

In our AI Scaling Laws article from late last year, we discussed how multiple stacks of AI scaling laws have continued to drive the AI industry forward, enabling greater than Moore's Law growth in model capabilities as well as a commensurately rapid reduction in unit token costs. These scaling laws are driven by training and inference optimizations and innovations, but advancements in compute capabilities transcending Moore's Law have also played a critical role. One this front, in the AI Scaling Laws article, we revisited the decades-long debate around compute scaling, recounting the end of Dennard Scaling in the late 2000s as well as the end of classic Moore's Law pace cost per transistor declines by the late 2010s. Despite this, compute capabilities have continued to improve at a rapid pace, with the baton being passed to other technologies such as advanced packaging, 3D stacking, new transistor types and specialized architectures such as the GPU. When it comes to AI and deep learni

NVIDIA Tensor Core Evolution: From Volta To Blackwell Amdahl’s Law, Strong Scaling, Asynchronous Execution, Blackwell, Hopper, Ampere, Turing, Volta, TMA Dylan Patel and Kimbo Chen Jun 23, 2025 ∙ Paid 24 2 Share In our AI Scaling Laws article from late last year , we discussed how multiple stacks of AI scaling laws have continued to drive the AI industry forward, enabling greater than Moore's Law growth in model capabilities as well as a commensurately rapid reduction in unit token costs. These scaling laws are driven by training and inference optimizations and innovations, but advancements in

Explore this link on the map →

saved by

related reading