What does this mean for the AI race? For example what if a newish company (newer than Google/Facebook/Microsoft/etc.) like Anthropic, Scale, Perplexity, or Stability is able to scrape together $5B USD funding and spend their hardware budget on these things. Say that can buy $1B of them and spend the rest on hackers and operating expenses (idk if that's realistic). So maybe they could purchase and operate like 20 of them. Say that they spend six months doing experimental things and then the next six months training their Tsar Model. If they follow the Chinchilla scaling laws and normal architectures, how good will these models be?

For AI race means it opens the door to a competitor. Could be AMD, Google, or Amazon. All which have offerings in this space.

However while the hardware isn’t cheap, it’s still likely not a blocker. Costs do inhibitor more experimental research.

Startup which will rent this compute when needed will likely have more advantage on AI front.

Selling shovels is good business, but doesn't compete directly in AI area.

I have no expertise in GPU System used for AI Learning, but would It be possible to buy a bunch of consumer cards and get the same performance? Or is this not possible because consumer cards go to 40 ish GB RAM and Models would not fit or „swapping“ like crazy and be slow.

Consumer cards only have PCIe 4.0, at most 24GB VRAM and the only recent model with NVLink, the RTX 3090, can only be connected to exactly one other card. It doesn't scale beyond that. So you are limited to PCIe 4.0 x16 speeds.

Technically the A6000 has 48 Gb of VRAM and works in 2-way NVLink.

The NVLink interconnect on all the GPUs is a huge part of it, and cannot come even remotely close to that bandwidth with consumer goods. Then the density of RAM to compute and power is huge. A single 4090 is 450 watts, for 24GB where this is 20x the memory for the same watts. 2.3Mw or so. If you say $0.14 / kwh, thats something like $325 / hour in power costs to run. Not counting additional cooling you are definitely going to need. And I am sure there is inefficiency this doesn't cover but 240v 10,000+ Amps for that?

Not the same. Not all problems can be efficiently divided among NUMA nodes with low bandwidth interconnects.

With that much memory I could probably run Crysis 4 and an Electron app side by side!