Nvidia GB300 Enters Mass Production: The Next Phase of AI Compute Wars

Honestly, every time I see a new Nvidia chip announcement, I instinctively do the math: how much compute can you get for how much money this time? GB300 didn’t disappoint - built on TSMC’s 3nm process with transistor density nearly 3x that of the H100, while keeping power consumption under 1000W. That efficiency improvement is real.

My personal take is that GB300’s launch signals the AI compute war has officially entered the “second half.” The first half’s rule was simple: whoever could manufacture more H100s would win. But the second half is different - the rule becomes: whoever can deliver more compute per watt. Who would’ve thought GPUs would one day compete on energy efficiency?

But what I find most interesting is the pricing. The H100 launched at $30-40K per chip. What will GB300 cost? Historically, new chips command premium prices at launch, then drop as production scales. But this time there’s a wild card: AMD’s MI350 is watching closely, leaving Nvidia less room for premium pricing than before.

I’m also curious about GB300’s inference performance. Training and inference are two different things - GB300’s architecture is heavily optimized for training, but how does it perform for inference? That’s still TBD. This is fascinating - raw compute doesn’t equal inference efficiency, just like horsepower doesn’t equal fuel efficiency.