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Comment by echelon | Hacker News Reader

When will we break past Nvidia's dominance of the field? Is AMD anywhere close to catching up? Other players? TPUs?

Not soon, it seems. Nvidia is so far ahead that they're artificially limiting their 4090's to half speed to fill a market niche.

https://x.com/realGeorgeHotz/status/1868356459542770087

Is there a possible way to unblow the fuse? I imagine it depends on the type of e-fuse used. The Athlon XP pencil trick probably won't work, haha. Curious if anyone has more information.

Fascinating, I had no idea this was even a thing. Simultaneously badass team green is far ahead and also a bummer to be artificially limited / segmented.

I wonder if the upcoming 5090 core will mostly be a fuse-intact 4090. I imagine nearly all of NVs current focus is on H200 and Blackwell and whatever else is in the pipeline rather than these "silly" little gamer cards which bring in comparably trivial financial resources.

/me *cries a tear*

Also interested in this ...

Mixed messages on that answer, but I'd like to know as well.

https://x.com/cognitivecompai/status/1868399108924592391

https://x.com/cognitivecompai/status/1868401738706993301

From my research it seems that restoring full GPU capabilities by repairing or circumventing a deliberately blown eFuse on an NVIDIA AD102 die is, for all practical purposes, impossible.

> Is there a possible way to unblow the fuse?

Maybe, but it's also possible that it wouldn't do anything. Other Nvidia boards (like the Tegra in the Switch) also come with arbitrarily disabled "dark silicon", but enabling the extra SOC hardware only causes the board to crash when using everything at once. It wouldn't surprise me if this was a binning measure, even though I also wouldn't be surprised if it was an arbitrary limit.

I assumed this was to make GPUs more affordable for users with gaming use cases in mind.

No, it is to make GPUs more expensive for users with professional use cases in mind.

In a way... I can actually see this as fair. What's the difference between the 4090 rtx and the 6000 ada? 5x the price for 2x the memory? Ridiculous. But then you have to factor in all the R&D dollars Nvidia poured into their compute/non-graphics ecosystem which now easily eclipses the gaming one, probably by a factor of 10 or more, and suddenly it doesn't seem so ridiculous. You either a) don't get 4090 level of a graphics card anymore... or b) you do get it, but only if it's nerfed for non-graphics uses... Nvidia wants its big R&D bucks back (and then some) and its gonna get em

It won’t happen by other company coming up with faster chips. It will happen by other company coming with cheaper chips and less energy demanding, dominating the low end and then reaching higher.

While Nvidia might have won the training market, it’s inference where the real money is.

Screw performance, just give me a consumer priced card that has oodles of RAM.

Strange to see no mention of TPUs...

TPUs are like the NPU of the training world. You take a bunch of extra time, money and dedicated silicon and end up with an ASIC that barely competes on equal terms with a similarly priced GPU. Unless you've got access to Nvidia's TSMC supply, you're probably not going to make a dent on their demand.

Additionally - TPUs are completely useless if AI goes out of style, unlike CUDA GPUs. The great thing about Nvidia's hardware right now is that you can truly use the GPU for whatever you want. Maybe AI falls through in 2026, and now those GPUs can be used for protein folding or crypto mining. Maybe crypto mining and protein folding falls through - you can still use most of those GPUs for raster renders and gaming too! TPUs are just TPUs - if AI demand goes away, your dedicated tensor hardware is dead weight.

Also TPU v1,v2 and v3 were ASICs, but since v4 they have added some new features so they have a lower performance/watt which is quite near Nvidia's power draw. I think Hopper is at 700W and TPU are around 600W.

Power draw doesn't matter in cloud. All that matters is performance/price for the task in hand.

In the cloud, opex == energy.

Which is why you don't send 4 TPUs to do 1 GPU's job.

If the TPUs cost less, that implies that they draw less power. If they cost more then nobody will use them.

They are overhyped and not as performant as Nvidia regardless of marketing.

the article says microsoft and meta bought more than 100k AMD each so maybe they're making inroads.

Meta is Nvidia shop for training and AMD for inferencing. Strict seperation between vendors.

Never? AMD can't write proper drivers...Most don't realize how far ahead NVIDIA is.

I think this is a vast understatement. Google has been using their own TPUs for a very long time now. I think they still have some GPUs from Nvidia, but it's marginal compared to their own silicon. Other big players are behind the curve on this front, but very much working to close the gap. These are companies with nearly infinite pockets, Google has shown that you can make it work without Nvidia, it's only a matter of time before others do it too.

See I absolutely dislike this thought that hyperscalars can easily beat nvidia. It is not their domain of expertise. Tpu are not where near GPU in performance. People really underestimate Nvidia's expertise and strengths.

And yet Google seems to be doing just fine while spending less on GPUs than other big tech, so reality doesn't seem to align with your comment.

https://www.hpcwire.com/2024/10/30/role-reversal-google-teas... Well they are still buying GPUs.

Mostly for external cloud customers so doesn't change my point - TPUs have been more than enough for Google to lead the pack.

They don't need to beat them on performance though. If you get half the performance at third the price you can just make more chips and be fine. It's not like Google is gonna run out of datacenter space.

TPU support is very minimal. Also XLA just does not compare to CUDA yet.

All of this may very well be true — but it doesn't matter. Google is getting results very similar to the rest of the pack, probably at a fraction of the cost. The implementation doesn't matter so long as you get the results.

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