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by aurareturn 1 hour ago
Broadcom already has a ton of IP for AI SoCs. I'm guessing the hard parts of this inference chip was already designed by Broadcom and OpenAI simply told Broadcom what it wanted. It's likely very similar to Google's TPU.

  Early testing shows that the first-generation accelerator will deliver performance per watt substantially better than current state-of-the-art
What is substantial here? Vera Rubin is shipping in volume later this year and it is expected to be 10x more power efficient for inference than Blackwell.[0] Even if they're already taped out the chip, getting bugs fixed, getting chips manufactured, getting HBM allocation, getting a rack design, hooking them up together, putting them in a data center will likely take at least another 12 months or likely more. By the time this chip is in data centers in volume, they're likely competing against Vera Rubin Ultra or maybe even Feynman.

Personally, I don't think OpenAI should have invested in this project. It's too early for them. They should have focused on models like Anthropic and win there. When they're profitable, they can take on these projects.

The risk here is very high for OpenAI because AI has a hard cap in energy. If you have a gigawatt, you should only install the best chips. If Nvidia's chips are better, then this is a wasted project and likely wasted billions.

[0]https://developer.nvidia.com/blog/scaling-token-factory-reve...

1 comments

Why do you assume Broadcom has a ton of IP for AI SoCs but hasn't done any of the other work around data center scale deployments?
They have. That's why OpenAI was able to get a working demo in 9 months. But going from a small scale system to a full fledged data center deployment is likely much harder.

I don't know how much of the things outside of the chip Broadcom has vs Google's proprietary tech that is not shared with Broadcom.

Nvidia's Vera Rubin has 6 unique chips working together in a single rack.[0]

[0]https://developer-blogs.nvidia.com/wp-content/uploads/2026/0...

I’m just happy to see diversity here; sometimes I feel like Nvidia is going to eat the world, with buying other fabs and branching out - or up, I guess - from chips and racks to models, frameworks, and end user stuff.