Speculation and Rumors: Nvidia Blackwell ...

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iroboto said:
Oh ok. So Jan 2025
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Yeah, they could probably launch some of them now but they seem to want as much 40-series stock cleared out as possible before the launch. Hopefully we’ll get at least one of the cards by end of January. Early word was they’d release the 5080 before the 5090, but unless it’s priced very competitively I’d think they’d want the halo card out first. In any case they’ll probably release within a few weeks of each other as usual - wouldn’t be surprised to see the rumored 5070 launch much later, in the spring. Of course political developments could affect all of this. I hope not but the possibility can’t be entirely discounted.
 
wccftech.com

NVIDIA "Blackwell" DGX B200 Listed, Prices Start At A Half A Million Dollars For The Top of The Line AI Hardware

NVIDIA's DGX B200 "Blackwell" AI server has witnessed its first apparent retail listing by the high-end server solution provider Broadberry
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The listing at Broadberry has put a price tag of $515,410.43 on the Blackwell DGX B200 AI system, with configuration options as well, mainly dealing with after-sale services. This is the first instance where we have seen NVIDIA's Blackwell AI product surface over the internet in the form of a retail listing, and while we are currently unaware of the supply situation, it is said that Blackwell will be initially confined, with a larger portion of shipments slated for the first quarter of next year.
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www.zach.be

How do Nvidia Blackwell GPUs train AI models with 4-bit math?

Bigger chips crunching smaller numbers.
www.zach.be www.zach.be
When you look at FP8 training performance on the spec chart below, B200 is 2.5x faster than Hopper -- which is only 1.25x faster on a per-die basis. How did they get to that “5x faster training” number? Well, B200 has another new feature to double per-die performance over Hopper: 4-bit arithmetic.

4 bits doesn’t seem like a lot. If you’re using those bits to represent integers, you can only count up to 16. But Nvidia’s GPUs feature some really clever technology to squeeze the most utility out of those 4-bit numbers. They’re called “mixed precision tensor cores,” and if you want to understand Nvidia’s dominance at AI, you need to understand how they work.
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The closer a network can get to being represented entirely with FP4 operations, the closer Blackwell’s training performance can get to that eye-popping 5x number Nvidia cited. And luckily, there’s already some research showing that networks can train with FP4 operations without significant loss of accuracy. If those results can scale to GPT-4-scale networks, then Nvidia has a huge advantage over other datacenter AI chips, which, as far as I can tell, don’t yet support these FP4 operations.
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Tkumpathenurple said:
How well does it run Crysis though?

On a genuine note, why have they gone with Intel rather than AMD for the CPU? I thought AMD performed better these days?
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Could simply be a matter of timing, i.e. what was available earlier for qualification/validation. And anyway the CPU performance of these clusters is not relevant, the CPU is almost an accessory. They will likely qualify newer generation processors for third party servers in the coming quarters.
 
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