Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Nvidia Grace CPU

nvidia.com

Post reply on HN
Print view View original

91–100 of 197 posts

Re: Nvidia Grace CPU

#91

Post by _delirium »

andrewstuart wrote: post #71
donatj wrote: post #56

Maybe it's just me, but it's just cool to see the CPU market competitive again for the first time since the late 90s.

I wonder why Intel never had a really good go at GPU's? It seems strange, given the demand.

Besides integrated GPUs for actual graphics usage that other comments mentioned, Intel did make some attempts at the GPGPU market. They had a design for a GPU aimed primarily at GPGPU workloads, Larrabee, that was never released [1], and adapted some of the ideas into Xeon Phi, a more CPU-like chip that was intended to be a competitor to GPUs, which was released but didn't gain a lot of market share [2].

[1] https://en.wikipedia.org/wiki/Larrabee_(microarchitecture)

[2] https://en.wikipedia.org/wiki/Xeon_Phi

Re: Nvidia Grace CPU

#92

Post by devmunchies »

qbasic_forever wrote: post #58
t0mas88 wrote: post #7

How likely is it that one of AWS / GCP / Azure will deploy these? Nvidia has some relationships there for the A100 chips.

Amazon has at least two generations of their own homebrew ARM chip, the Graviton. They offer it for people to rent and use in AWS, and publicly stated they are rapidly transitioning their internal services to use it too. In my experience Graviton 2 is much cheaper than x86 for typical web workloads--I've seen costs cut by 20-40% with it.

> their own homebrew ARM chip

are they going through TSMC like NVIDIA or are they using Samsung?

Re: Nvidia Grace CPU

#93

Post by [deleted] »

bullen wrote: post #85

I think we're all missing the forest because all the cores are in the way: The contention on that memory means that only segregated non-cooporative as in not "joint parallel on the same memory atomic" will scale on this hardware better than on a 4-core vanilla Xeon from 2018 per watt. So you might aswell buy 20 Jetson Nanos and connect them over the network. Let that sink in... NOTHING is improving at all... there is…

[deleted]

Re: Nvidia Grace CPU

#94

Post by simulate-me »

bullen wrote: post #85

I think we're all missing the forest because all the cores are in the way: The contention on that memory means that only segregated non-cooporative as in not "joint parallel on the same memory atomic" will scale on this hardware better than on a 4-core vanilla Xeon from 2018 per watt. So you might aswell buy 20 Jetson Nanos and connect them over the network. Let that sink in... NOTHING is improving at all... there is…

Performance per watt isn’t so useful for a GPU. People training ML algorithms would gladly increase power consumption if they could train larger models or train models faster.

Re: Nvidia Grace CPU

#95

Post by Gigachad »

arebop wrote: post #90
Gigachad wrote: post #88

Earlier quoted context omitted.

The products don't get produced in order. The high value products get priority and continuously bump out low value chips like those on the RPI. Not sure what the cost of this Grace chip is but it looks to be targeting high value users so it gets priority. Notice how there is no shortage of chips for iPhones, because Apple just buys the capacity at whatever cost it takes.

Though, there is a shortage of m1 MacBooks. Is it really because they are low value (margin?) products relative to iPhone? I'm not sure.

Not much of a shortage. I just checked and they are all available for pickup right now at my local small city store. Compared to other products they are still extremely available.

Re: Nvidia Grace CPU

#96

Post by cma »

bullen wrote: post #85

I think we're all missing the forest because all the cores are in the way: The contention on that memory means that only segregated non-cooporative as in not "joint parallel on the same memory atomic" will scale on this hardware better than on a 4-core vanilla Xeon from 2018 per watt. So you might aswell buy 20 Jetson Nanos and connect them over the network. Let that sink in... NOTHING is improving at all... there is…

Aren't you are ignoring use cases where all cores read shared data, but rarely contentiously write to it. You should get much more read bandwidth and latency than over a network.

Re: Nvidia Grace CPU

#97

Post by bullen »

simulate-me wrote: post #94
bullen wrote: post #85

I think we're all missing the forest because all the cores are in the way: The contention on that memory means that only segregated non-cooporative as in not "joint parallel on the same memory atomic" will scale on this hardware better than on a 4-core vanilla Xeon from 2018 per watt. So you might aswell buy 20 Jetson Nanos and connect them over the network. Let that sink in... NOTHING is improving at all... there is…

Performance per watt isn’t so useful for a GPU. People training ML algorithms would gladly increase power consumption if they could train larger models or train models faster.

And that's exactly my point: they can't. Power does not solve contention and latency! It's over, permanently... (or atleast until some photon/quantum alternative, which honestly we don't have the energy to imagine, let alone manufacture, anymore)

Re: Nvidia Grace CPU

#98

Post by belval »

ksec wrote: post #5

This is interesting. So without actually targeting a specific Cloud / server market for their CPU, which often ends with a chicken and egg problem with HyperScaler making their own Design or Chip. Nvidia manage to enter the Server CPU market leveraging their GPU and AI workload. All of a sudden there is real choice of ARM CPU on Server. ( What will happen to Ampere ? ) The LPDDR5X used here will also be the first to…

AWS Graviton aren't toys, they work pretty well for a wide range of workloads

Re: Nvidia Grace CPU

#99

Post by p1necone »

arebop wrote: post #90
Gigachad wrote: post #88

Earlier quoted context omitted.

The products don't get produced in order. The high value products get priority and continuously bump out low value chips like those on the RPI. Not sure what the cost of this Grace chip is but it looks to be targeting high value users so it gets priority. Notice how there is no shortage of chips for iPhones, because Apple just buys the capacity at whatever cost it takes.

Though, there is a shortage of m1 MacBooks. Is it really because they are low value (margin?) products relative to iPhone? I'm not sure.

I was pretty surprised by the low prices of m1 macbooks when even the lowest end models perform so much better than the high end of previous models. I'm sure Apple is spending less money on manufacturing them now that they're not going through Intel, but I would have expected them to just keep charging the same and eaten the profit margin themselves.

Re: Nvidia Grace CPU

#100

Post by bullen »

cma wrote: post #96
bullen wrote: post #85

I think we're all missing the forest because all the cores are in the way: The contention on that memory means that only segregated non-cooporative as in not "joint parallel on the same memory atomic" will scale on this hardware better than on a 4-core vanilla Xeon from 2018 per watt. So you might aswell buy 20 Jetson Nanos and connect them over the network. Let that sink in... NOTHING is improving at all... there is…

Aren't you are ignoring use cases where all cores read shared data, but rarely contentiously write to it. You should get much more read bandwidth and latency than over a network.

Sure, but my point is: why cram more and more cores into the same SoC if they can't talk to each other more efficiently than separate computers over ethernet?
Post reply on HN