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NVIDIA is pretty established but there's also Intel, AMD, Google to contend with. Sure Cerebras is unique in that they make one large chip out of the entire waf

NVIDIA is pretty established but there's also Intel, AMD, Google to contend with. Sure Cerebras is unique in that they make one large chip out of the entire wafer but nothing prevents these other companies from doing the same thing. Currently they are choosing not to because of wafer economics but if they chose to, Cerebras would pretty much lose their advantage. https://www.servethehome.com/cerebras-wse-3-ai-chip-launched-56x-larger-than-nvidia-h100-vertiv-supermicro-hpe-qualcomm/ https://www.servethehome.com/cerebras-wse-3-ai-chip-launched... 56x the size of H100 but only 8x the performance improvement isn't something I would brag about. I expected much higher performance since all processing is on one wafer. Something doesn't add up (I'm no system designer). Also, at $3.13 million per node, one could buy 100 H100s at $30k each (not including system, cooling, cluster, etc). Based on price/performance Cerebras loses IMO.

pants2 2y ago
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.
- BuckYeah 2y ago
  Where/how did you learn of the hedge fund usages?
  - bee_rider 2y ago
    If the poster never comes back, I think it is fair to assume it is just a reasonable guess, right?
fnordpiglet 2y ago
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 expensive when considering enterprise grade data center hardware. I don’t think their value add is simple “single wafer” with all other variables the same. In fact I think the block and system that gets the most out of that form factor is the secret sauce and not as easily replicated - especially since the innovations are almost certainly protected by an enormous moat of patents and guarded by a legion of lawyers.
- 7e 2y ago
  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.
  - twothreeone 2y ago
    https://finance.yahoo.com/news/cerebras-launches-world-fastest-ai-160000579.html https://finance.yahoo.com/news/cerebras-launches-world-faste...
    - rajnathani 2y ago
      Nothing for the training part of MLPerf's benchmark. If they're competing just on inference, then they have stiff competition from specialized NPU-for-inference makers like Hailo (see: it's even part of the official Raspberry Pi AI kit), Qualcomm, tons of other players, and also some players using optics instead of electrons for inference such as Lightmatter, and also SIMD on highly abundant CPU servers which are never in shortage unlike GPUs (and have recently gotten support for specialized inference ops besides simply SIMD ones).
    - 7e 2y ago
      This isn't a benchmark, it's a press release. MLPerf has an inference component so they could have released numbers, but they chose not to. 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.
      - txyx303 2y ago
        MLPerf brings in exactly zero revenue. If they have sold every chip they can make for the next 2+ years, why would they be diverting resources to MLPerf benchmarking? Artificial analysis does good API provider inference benchmarking and has evaluated Cerebras, Groq, Sambanova, the many Nvidia-based solutions, etc. IMO it makes way more sense to benchmark actual usable end points rather than submit closed and modified implementations to mlcommons. Graphcore had the fastest BERT submission at one point (when BERT was relevant lol) and it didn't really move the needle at all.
deleted 2y ago
[deleted]
yieldcrv 2y ago
they don’t need an advantage, they just need orders and inventory get extorted by nvidia sales people for a 2026 delivery date that gets pushed out if you say anything about it or decline cloud services or another provider delivering earlier thats what the market wants, and even then, who cares? this company is trying to IPO at whay valuation? this article didnt say but the last valuation was like $1.5bn? so you mean a 300x of delta between this and Nvidia’s valuation if these guys get a handful of orders? ok
- rwmj 2y ago
  At the end of the day it's all made in the same factory. If nVidia have problems delivering then so do Cerebras.
artemisart 2y ago
Correction: it's 8x the TFLOPS of a DGX (8 H100), not 1 H100. But it's true that if it stays at $3M it's probably too much and I don't think the memory bottleneck on gpus is large enough to justify this price/performance.
- throwup238 2y ago
  The company started in 2015 so I think they are (were?) banking on SRAM scaling better than it has in recent years.
- bee_rider 2y ago
  If you have a problem that you can’t easily split up into 64 chunks, I guess it makes more sense, right?
- Tepix 2y ago
  So, the corrected statement is: "56x the size of H100 but only 64x the performance improvement" Doesn't sound too shabby.
dzhiurgis 2y ago
> wafer economics What are they? Is this related to defects? Can't they disable parts of defective chip just like other CPUs do? Sounds cheaper than cutting up and packaging chips individually!
- donavanm 2y ago
  Process development, feature size, and ultimate yield are probably what theyre after. Yes, for the past 30+ years everyone has used a combination of disabling (“fusing”) unused/unreliable logic on the die. In addition everyone also “bins” the chips from the same wafer to different SKUs based on stable clock speed, available/fused components, test results, etc. This can be very effective in increasing yield and salable parts. My recollection is that theres speculation cerebras is building in significant duplicate features to account for defects. They cant “bin” their wafers in the same way as packaged chips. That will reduce total yield/utilization of the surface area. The actual packaging steps are relatively low tech/cost compared to the semiconductor manufacturing. Theyre commonly outsourced somwhere like malaysia or thailand.
jfoster 2y ago
> 56x the size of H100 but only 8x the performance improvement isn't something I would brag about. It doesn't sound like it's too bad for a 9 year old company. Nvidia had a 20-year head start. I would expect that they will continue to shrink it and increase performance. At some point, that might become compelling?
- statguy 2y ago
  Nvidia is also going to keep improving, so it will be a moving target.
  - jfoster 2y ago
    That's true, but the advantage of having a head start does eventually diminish. They won't catch up to Nvidia in the next couple of years, but they could eventually be a real competitor.
Zandikar 2y ago
Comparing a WSE-3 to a H100 without considering the systems they go in or the systems, cooling, networking, etc that supports them means little when doing cost analysis, be it CapEx or TCO. A better (but still flawed) comparison would be a DGX H200 (a cluster of H100's and their essential supporting infra) to a CS-3 (a cluster of WSE-3's and their essential supporting infra in a similar form factor/volume of a DGX H200). Now, is Cerebras going to eventually beat Nvidia or at least compete healthily with Nvidia and other tech titans in the general market or a given lucrative niche of it? No idea. That'd be a cool plot twist, but hard to say. But it's worth acknowledging that investing in a company and buying their products are two entirely separate decisions. Much of silicon valleys success stories are a result of people investing in the potential of what they could become, not because they were already the best on the market, and for nothing else, Cerebras approach is certainly novel and promising.
hulitu 2y ago
> Sure Cerebras is unique in that they make one large chip out of the entire wafer I'm sure tgey test it thoroughly. /s

pants2 2y ago

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.

BuckYeah 2y ago
Where/how did you learn of the hedge fund usages?
- bee_rider 2y ago
  If the poster never comes back, I think it is fair to assume it is just a reasonable guess, right?

fnordpiglet 2y ago

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 expensive when considering enterprise grade data center hardware. I don’t think their value add is simple “single wafer” with all other variables the same. In fact I think the block and system that gets the most out of that form factor is the secret sauce and not as easily replicated - especially since the innovations are almost certainly protected by an enormous moat of patents and guarded by a legion of lawyers.

7e 2y ago
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.
- twothreeone 2y ago
  https://finance.yahoo.com/news/cerebras-launches-world-fastest-ai-160000579.html https://finance.yahoo.com/news/cerebras-launches-world-faste...
  - rajnathani 2y ago
    Nothing for the training part of MLPerf's benchmark. If they're competing just on inference, then they have stiff competition from specialized NPU-for-inference makers like Hailo (see: it's even part of the official Raspberry Pi AI kit), Qualcomm, tons of other players, and also some players using optics instead of electrons for inference such as Lightmatter, and also SIMD on highly abundant CPU servers which are never in shortage unlike GPUs (and have recently gotten support for specialized inference ops besides simply SIMD ones).
  - 7e 2y ago
    This isn't a benchmark, it's a press release. MLPerf has an inference component so they could have released numbers, but they chose not to. 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.
    - txyx303 2y ago
      MLPerf brings in exactly zero revenue. If they have sold every chip they can make for the next 2+ years, why would they be diverting resources to MLPerf benchmarking? Artificial analysis does good API provider inference benchmarking and has evaluated Cerebras, Groq, Sambanova, the many Nvidia-based solutions, etc. IMO it makes way more sense to benchmark actual usable end points rather than submit closed and modified implementations to mlcommons. Graphcore had the fastest BERT submission at one point (when BERT was relevant lol) and it didn't really move the needle at all.

deleted 2y ago

[deleted]

yieldcrv 2y ago

they don’t need an advantage, they just need orders and inventory get extorted by nvidia sales people for a 2026 delivery date that gets pushed out if you say anything about it or decline cloud services or another provider delivering earlier thats what the market wants, and even then, who cares? this company is trying to IPO at whay valuation? this article didnt say but the last valuation was like $1.5bn? so you mean a 300x of delta between this and Nvidia’s valuation if these guys get a handful of orders? ok

rwmj 2y ago
At the end of the day it's all made in the same factory. If nVidia have problems delivering then so do Cerebras.

artemisart 2y ago

Correction: it's 8x the TFLOPS of a DGX (8 H100), not 1 H100. But it's true that if it stays at $3M it's probably too much and I don't think the memory bottleneck on gpus is large enough to justify this price/performance.

throwup238 2y ago
The company started in 2015 so I think they are (were?) banking on SRAM scaling better than it has in recent years.
bee_rider 2y ago
If you have a problem that you can’t easily split up into 64 chunks, I guess it makes more sense, right?
Tepix 2y ago
So, the corrected statement is: "56x the size of H100 but only 64x the performance improvement" Doesn't sound too shabby.

dzhiurgis 2y ago

> wafer economics What are they? Is this related to defects? Can't they disable parts of defective chip just like other CPUs do? Sounds cheaper than cutting up and packaging chips individually!

donavanm 2y ago
Process development, feature size, and ultimate yield are probably what theyre after. Yes, for the past 30+ years everyone has used a combination of disabling (“fusing”) unused/unreliable logic on the die. In addition everyone also “bins” the chips from the same wafer to different SKUs based on stable clock speed, available/fused components, test results, etc. This can be very effective in increasing yield and salable parts. My recollection is that theres speculation cerebras is building in significant duplicate features to account for defects. They cant “bin” their wafers in the same way as packaged chips. That will reduce total yield/utilization of the surface area. The actual packaging steps are relatively low tech/cost compared to the semiconductor manufacturing. Theyre commonly outsourced somwhere like malaysia or thailand.

jfoster 2y ago

> 56x the size of H100 but only 8x the performance improvement isn't something I would brag about. It doesn't sound like it's too bad for a 9 year old company. Nvidia had a 20-year head start. I would expect that they will continue to shrink it and increase performance. At some point, that might become compelling?

statguy 2y ago
Nvidia is also going to keep improving, so it will be a moving target.
- jfoster 2y ago
  That's true, but the advantage of having a head start does eventually diminish. They won't catch up to Nvidia in the next couple of years, but they could eventually be a real competitor.

Zandikar 2y ago

Comparing a WSE-3 to a H100 without considering the systems they go in or the systems, cooling, networking, etc that supports them means little when doing cost analysis, be it CapEx or TCO. A better (but still flawed) comparison would be a DGX H200 (a cluster of H100's and their essential supporting infra) to a CS-3 (a cluster of WSE-3's and their essential supporting infra in a similar form factor/volume of a DGX H200). Now, is Cerebras going to eventually beat Nvidia or at least compete healthily with Nvidia and other tech titans in the general market or a given lucrative niche of it? No idea. That'd be a cool plot twist, but hard to say. But it's worth acknowledging that investing in a company and buying their products are two entirely separate decisions. Much of silicon valleys success stories are a result of people investing in the potential of what they could become, not because they were already the best on the market, and for nothing else, Cerebras approach is certainly novel and promising.

hulitu 2y ago

> Sure Cerebras is unique in that they make one large chip out of the entire wafer I'm sure tgey test it thoroughly. /s