Blackwell: Nvidia's GPU
16 comments
·June 29, 2025CalChris
gchadwick
> It has two layers
Where did you get that from? Pretty sure it's a single planar set of transistors. Those transistors are manufactured using multiple layers of mask.
FinFET transistors are described as 3D or non-planar but crucially this isn't allowing transistor on transistor stacking you've just got the gate structure of the FinFET poking out above the plane of the rest of the transistors.
Silicon on silicon die stacking is a possibility but limits your power and GPUs run very hot so it's not an option for them.
dist-epoch
You also need space for wires, ..., etc, right? It's not just transistors.
gchadwick
The wires sit on top of the transistors. Many layers of them in a modern process.
However you can't always pack the transistors as dense as you would like because you can't fit the wiring for them in above at the same density.
Plus there are various 'design rules' that constrain how things get placed. These are needed to ensure manufacturing is successful and achieved good yield. An important set of rules are the 'antenna rules' that requires the insertion of antenna diodes (using silicon reducing transistor density) to prevent circuitry being destroyed during manufacturing: https://www.zerotoasiccourse.com/terminology/antenna-report/
CalChris
The wires didn't fit on the back of the envelope.
a_wild_dandan
I love this retort and I'm stealing it.
amelius
The wires run over the transistors.
ksec
I heard there is still trouble to buy consumer grade Nvidia GPU. At this point I am wondering if it is Gaming market demand, AI, or simply a supply issue.
On another note I am waiting for Nvidia's entry to CPU. At some point down the line I expect the CPU will be less important, ( relatively speaking ) and Nvidia could afford to throw a CPU in the system as bonus. Especially when we are expecting ARM X930 to rival Apple's M4 in terms of IPC. CPU design has become somewhat of a commodity.
Incipient
My understanding is it's the AI demand and willingness to pay crazy money for wafer that makes consumer GPUs a significantly less attractive product to produce.
I don't have really solid evidence, just semi-anecdotal/semi-reliable internet posts:
Eg. https://www.tomshardware.com/tech-industry/more-than-251-mil...
Nvidia as a whole has been fairly anti-consumer recently with pricing, so I wouldn't be banking on them for a great cpu option. Weirdly Intel is in the position where they have to prove themselves, so hopefully they'll give us some great products in the next 2-5 years - if they survive (think the old lead-up-to-ryzen era for amd)
KronisLV
> Nvidia as a whole has been fairly anti-consumer recently with pricing, so I wouldn't be banking on them for a great cpu option.
If they’re swimming in the AI cash and the consumer GPU segment isn’t that important (https://www.visualcapitalist.com/nvidia-revenue-by-product-l...) then why on earth couldn’t they do less price gouging?
It feels a bit like the Intel Core Ultra desktop CPU launch where the prices were the critical factor that doomed an otherwise pretty okay product. At least Intel's excuse is that they’re closer to going under than before, even if their GPUs were pretty fairly priced anyways.
It’s almost like everyone complains about their prices and the fact that they’re releasing 8 GB cards… and then still go and give them money anyways.
jonas21
> I am waiting for Nvidia's entry to CPU.
Haven't they already started doing this with Grace and GB10?
- https://www.nvidia.com/en-us/data-center/grace-cpu/
- https://nvidianews.nvidia.com/news/nvidia-puts-grace-blackwe...
wtallis
Their Grace datacenter CPU is basically a chip where they put down all the LPDDR5 memory controllers (albeit curiously slow), NVLINK and PCIe IOs they needed around the perimeter, and then filled in the interior with boring off the shelf ARM cores. It's basically an IO and memory expander that happens to run Linux.
GB10 when it ships might be more interesting, since it'll go into systems that need to support use cases other than merely feeding a big GPU ML workloads. But it sounds like the CPU chiplet at least was more or less outsourced to Mediatek.
xl-brain
The micro center in my neighborhood has hundreds of 5090s in stock. I'm not sure its as hard as it used to be.
dist-epoch
Why doesn't NVIDIA also build something like Google TPU, a systolic array processor? Less programmable, but more throughput/power efficiency?
It seems there is a huge market for inference.
AlotOfReading
Nvidia tensor cores are small systolic arrays. They'd have to throw out a lot of their ecosystem investments and backwards compatibility to make effective use of them as the main GPU compute, and there's really no need given how competitive their chips are right now.
aurareturn
Less programmable, but more throughput/power efficiency?
Traditional GPUs like Blackwell that can do anything and have backwards compatibility.
Less programmable and more ASIC-like inference chips like Google's TPUs. Inference market is going to be multiple times bigger than training soon.
The Nvidia technical brief says 208 billion transistors.
https://resources.nvidia.com/en-us-blackwell-architecture
Blackwell uses the TSMC 4NP process. It has two layers. A very back of the envelope estimate:
NB: process feature size does not equal transistor size. Process feature size doesn't even equal process feature size.