New AI Supercomputer Outperforms NVIDIA

In this video I discuss New Cerebras Supercomputer with Cerebras's CEO Andrew Feldman.
Timestamps:
00:00 - Introduction
02:15 - Why such a HUGE Chip?
02:37 - New AI Supercomputer Explained
04:06 - Main Architectural Advantage
05:47 - Software Stack NVIDIA CUDA vs Cerebras
06:55 - Costs
07:51 - Key Applications & Customers
09:48 - Next Generation - WSE3
10:27 - NVIDIA vs Cerebras Comparison

Mentioned Papers:
Programming at Scale:

Mentioned Video:

[tanzeelrahman]

Your content is always very special and informative. You tend to choose topics that are not commonly found on other channels. The most important thing is the way you explain complex concepts so easily; that's truly awesome.

[CircuitSageMatheus]

Awesome content, nowadays is very difficult to find channels rich in information like yours! Cheers to you for a job well done! 👏

[pbananaandfriends]

The sheer compute power of this chip are promising a new era in AI technology. I’m eager to see how this will be utilized in various applications. Kudos to the team behind this innovation!

[jp]

It seems like an apples to oranges comparison. Put it against GH200 Superpod with 256 Grace Hopper Superchips. That is Nvidias latest offering. It's not only fast, but energy efficient.

[ZoOnTheYT]

Another awesome video Ana! Doing direct interviews is a great addition to your repertoire. I have an interest in AI as a social science person. A lot of videos either go way beyond my ability to comprehend, or are filled with superfluous information just to fill time. You consistently put out interesting and coherent information, that I also trust is valid, because of your background.

[aseeldee.]

This is very cool!
Thank you for keeping us up to date with the AI evolution!

[RocktCityTim]

If you ignore politics and AI conspiracies, it's a great time to be alive! Thank you for sharing these positive breakthroughs.

[junpengqiu]

wonderful video, did some research about cerebras innovative and found out they really have done different and valuable things.
"wafer scale engine" is what cerebras been known for, unlike traditional GPU, it is produced on an entire wafer. Conventionally, multiple cpu or gpu are 'printed' by EV on a single wafer, and later processes will cut them off the wafer. Therefore, one reason cerebras is delivering much better performance is because its 'GPU' is bigger.
But this also leads to one problem: its even harder to produce than NVDIA GPUs, wafer often comes with defects, individual defected chips from conventional manufacture technique can be discarded. However, cerabras wafer scale engine needs the whole wafer to have no defects. In addtion, heat dissipation, even powering across whole surface are big challenges.
Right now, cerebras is cheaper because it's not yet that popular, once market sees advantages from their super computer, their price can go higher than h100 since they are really difficult to make under current tech level.

[MrWingman]

Maybe I'm not looking hard enough, but this is the only place I've found good, well summarized info on AI hardware progress. Thanks Anastasi! 😊

[MichaelLloydMobile]

OMG...
I had to watch this video because your introductory image is adorable!

[solidreactor]

I am very interested in Cerebras and Tenstorrent, where they seem to be the most viable alternative to Nvidia, both being companies that makes AI chip that is very scalable.
The interesting differentiation between Cerebras and Tenstorrent is that Cerebras started with big chips working their way down (in a sense with enabling PyTorch compatibility) while Tenstorrent works from small chips and evolutionary works their way up.
It's interesting to see these different contrasting startup philosophies work in the same industry having basically the same main competitors. Hope to see you cover these two companies in future videos.

[SocialMediaSoup]

I have no idea what she is talking about, but I keep watching her videos.

[Arthur-uevz]

Thank you for doing these videos and helping the rest of us to see what's going on in the world of AI and computing in general.
I appreciate your efforts 😊

[GeinponemYT]

Never heard of this channel before, until it just popped up on my homepage. And I'm glad it did: great, clear information, with appropriate graphics (when needed), very in depth, but still understandable.
One minor piece of constructive feedback: maybe tweak your audio settings a bit to decrease the harsh 's' sounds. I'm using heaphones, and your 's'-es are a bit uncomfortable. Otherwise: great video!

[dchdch]

Wow, nice summary. I was actually wondering how they utilise all those wafer scale engines. Now it is clear. Thank you !

[chillcopyrightfreemusic]

Fantastic video I just subscribed. Mr. Feldman was speaking my mind when addressing the tokenization of the arabic language. I don't speak arabic sadly but have been trying to find good models to handle it and found that only gpt4 and bloom were decent. I think his company is on to something forging connections to the gulf. Great video thank you!

[JMeyer-qjpv]

The bane of wafer scale computing has always been that some percentage of the wafer will have defects and be unusable. Does Cerebras has some way around that problem? There was a famous attempt at this back in the 80's and the company couldn't solve the problem and went bankrupt (Trilogy Systems).

[CYIERPUNK]

what a time to be alive XD ; luv to see the competition heat up between these top tier tech firms and the smaller startups that are rocking the boat =]

[willykang]

Thank you for your deeper introduction on Cereras!!! I won’t know this despite I stayed around Fremont and Santa Clara last month if I didn’t get into it much deeper…😄

[woolfel]

it's great to see so many people and companies working on AI hardware, but without a full software stack, it won't be a credible competitor to NVidia. As ML technology advances, they'll have to make sure their compiler handles the workload scheduling efficiently. That's not an easy task.