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Ahh that explains it, thanks. Seems like a potentially large problem given their strategy.
They could use something like GCRAM[1] to double capacity if they had to...but it's not clear how much worse performance would be. [1] https://raaam-tech.com/products/
https://www.eng.biu.ac.il/fishale/files/2020/12/A-1-Mbit-Ful...
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I think the wafer itself isn’t the whole deal. If you watch their videos and read the link you posted the wafer size allows them to stack them in a block with integrated power and cooling at a higher density than blades and attach enormous amounts of memory. Not including the system, cooling, cluster, etc seems like a relatively unfair comparison too given the node includes all of those things - which are very expens…
Can their system attach memory? from what I read, it doesn't seem to be able to: https://www.reddit.com/r/mlscaling/comments/1csquky/with_waf...
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I guess it's a software problem. Without optimized implementations their performance will look like shit, even if their chip were years ahead of the competition. Building efficient implementations with an immature ecosystem and toolchain doesn't sound like a good time. But yeah, huge red flag. If they can't get their chip to perform there's no hope for customers.
This hypothesis is an eerily exact instance of the tinygrad (tinycorp) thesis, along the lines of “nvidia’s chip is better than yours. If you can’t make your software run well on nvidia’s chip, you have no hope of making it run well on your chip, least of all the first version of your chip.” That’s why tinycorp is betting on a simple ML framework (tinygrad, which they develop and make available open source) whose pro…
This sounds easy in theory, but in reality, based on current models, the implementations are often tuned to make them work fast on the chip. As an engineer in the ML compiler space, I think this idea of just using small primitives, which comes from the compiler / bytecode world, is not going to yield acceptable performance.
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Agreed, it just seems like Nvidia chips are going to be easier to produce at scale. Cerberas will be limited to a few niche use-cases, like HFT where hedge funds are using LLMs to analyze SEC filings as fast as possible.
Where/how did you learn of the hedge fund usages?
I’m going to go ahead and predict this flubs long term. Not only is what they are doing very challenging, I’ve had some random brokerage house reach out to me multiple times about investing in this IPO. When your IPO process resorts to cold calling I don’t think it’s a good sign. Granted I have some associations with AI startups I don’t think that had anything to do with the outreach from the firm.
You do realize that brokerages earn commissions on selling shares, so why wouldn't they contact people who may be interested?
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They could use something like GCRAM[1] to double capacity if they had to...but it's not clear how much worse performance would be. [1] https://raaam-tech.com/products/
The performance doesn't look great (yet). See Fig. 7 https://www.eng.biu.ac.il/fishale/files/2020/12/A-1-Mbit-Ful...
[1]https://cerebras.ai/blog/cerebras-architecture-deep-dive-fir...
[2]https://www.eenewseurope.com/en/raaam-signs-lead-licensee-fo...
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This hypothesis is an eerily exact instance of the tinygrad (tinycorp) thesis, along the lines of “nvidia’s chip is better than yours. If you can’t make your software run well on nvidia’s chip, you have no hope of making it run well on your chip, least of all the first version of your chip.” That’s why tinycorp is betting on a simple ML framework (tinygrad, which they develop and make available open source) whose pro…
> That’s why tinycorp is betting on a simple ML framework (tinygrad, which they develop and make available open source) whose promise is, due to the few operations needed by the framework: it’ll be very easy to get this software to run on a (eg your) new chip and then you can run ML workloads. This sounds easy in theory, but in reality, based on current models, the implementations are often tuned to make them work fa…
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At the end of the day, Cerebras has not submitted any MLPerf results (of which I am aware). That means they are hiding something. Something not very competitive. So, performance is iffy. Density for density sake doesn’t matter since clusters are power limited.
https://finance.yahoo.com/news/cerebras-launches-world-faste...
At the end of the day it's all about performance per dollar/TCO, too, not just raw perf. A standardized benchmark helps to evaluate that.
My guess is that they neglected the software component (hardware guys always disdain software) and have to bend over backwards to get their hardware to run specific models (and only those specific models) well. Or potentially common models don't run well because their cross-chip interconnect is too slow.
On the one hand, the financials are terrible for an IPO in this market. On the other, Nvidia is worth 3trn so they can sell a pretty good dream of what success looks like to investors. Personally I would expect them to get a valuation well about the 4bln from the 2021 round, despite the financials not coming close to justifying it.
Saying the financials are terrible is a bit of a stretch. Rapidly growing revenue, decreasing loss/share and a loss/share similar to other companies that IPO'ed this year. The more concerning thing is just not having diversity of revenue, since most of it comes from G42.