NVIDIA has once again set the bar for AI hardware with the announcement of its Blackwell architecture. The B200 GPU boasts 208 billion transistors and delivers up to 20 petaflops of FP4 power. This is a significant jump from the previous H100 generation, specifically optimized for training models with trillions of parameters.
The Blackwell design introduces a second-generation Transformer Engine which uses micro-scaling formats to double the compute and model size that can be supported. This efficiency is crucial as the industry shifts from training to inference, where cost and energy consumption become the primary bottlenecks for large-scale deployment.
Cloud providers like AWS, Google Cloud, and Microsoft Azure have already lined up to integrate Blackwell into their data centers. This move suggests that the race for sovereign AI and massive-scale foundation models is only accelerating, requiring an infrastructure that can handle the sheer volume of tokens processed every second.
