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Meta's open AI hardware vision

(engineering.fb.com)
240 points GavCo | 3 comments | 15 Oct 24 18:08 UTC | HN request time: 2.507s | source
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m_ke ◴[15 Oct 24 19:20 UTC] No.41852127[source]▶
I wonder when Meta, Microsoft and OpenAI will partner on an open chip design to compete with NVIDIA.

They’re all blowing billions of dollars on NVIDIA hardware with like 70% margin and with triton backing PyTorch it shouldn’t be that hard to move off of CUDA stack.

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infecto ◴[15 Oct 24 19:27 UTC] No.41852200[source]▶
What is a large mount of money to you, is not that significant to some of these companies. I suspect for the vast majority of these companies, it still represents a small expense. General sentiment is that there is probably overspending in the area but its better to spend it and not risk being left behind.
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1. m_ke ◴[15 Oct 24 21:57 UTC] No.41853535[source]▶
Half of NVIDIA's 2nd quarter revenue (30 billion) came from 4 customers, with Microsoft and Meta already having spent 40-60 billion each on GPU data centers (of which most goes to NVIDIA). "Open"AI just raised a few billion and is supposedly planning on building their own training clusters soon.

For a small fraction of that they could poach a ton of people from NVIDIA and publish a new open chip spec that anyone could manufacture.

https://www.fool.com/investing/2024/09/12/46-nvidias-30-bill...

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2. infecto ◴[16 Oct 24 01:48 UTC] No.41854859[source]▶
That again underestimates the challenges in that undertaking. After all these costs are still drops on the bucket. Why distract yourself from your business to go and build chips.

They all use SFDC, should they go and create and open source sales platform?

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3. m_ke ◴[16 Oct 24 03:29 UTC] No.41855334[source]▶
https://developers.facebook.com/blog/post/2021/09/07/eli5-op...

That's exactly what they id with their server design.

I'm saying come up with an open standard for tensor processing chips, with open drivers and core compute libraries, then let hardware vendors innovate and compete to drive down the price.

Meta spent like 10% of their revenue on ML hardware, it's not a drop in a bucket and with model scaling and large scale deployment these costs are not going down. https://www.datacenterdynamics.com/en/news/meta-to-operate-6...