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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?

by aurareturn22 hours ago|

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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...

by threecheese21 hours ago|

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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.

by surajrmal19 hours ago|

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I thought most of the Google tpu magic is on wiring up these chips into supercomputer like clusters with specialized interconnects and whatnot. The chips themselves are less interesting in isolation.

by luma17 hours ago|

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I know nothing of what is happening here but Broadcom has a lot of IP in high speed/low latency data transfer from chip to datacenter scales.

by AtlasBarfed2 hours ago|

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"Substantial" seems like a damning word.

So one of my pet theories I haven't seen in general discourse is that AI came from the massive vector processing jump available commercially in GPUs when it left CPU bound processing behind. That's a factor of 100x-1000x of processing power.

AI is not-quite-there, and to get even another leap might take another 10-100x processing power.

Now... what? ASICs probably won't deliver even a 10x? There's only so much you get out of node shrinks.

"Substantial" doesn't even mean twice IMO. "Substantial" almost sounds like ... 15% better?