cloud.google.com
cloud.google.com
aws.amazon.com
www.cerebras.net
Thanks for the excellent reply and information. Cerebras is a company I have followed off and on and will get back to looking carefully at. Who do you feel leads in memory for AI (Micron, Samsung) and outside of ASML, provides key equipment and materials, AMAT?There's a lot more going on in AI chip development than Nvidia and AMD. GPUs happen to be useful for recurrent neural networks, like transformers, which are used by large language model AI software. (You can do a search for all of these terms, and get explanations for what they mean. But keep in mind that understanding the explanations does require some knowledge of moderately advanced computer science concepts.) GPUs are designed to a general computing model called Single Instruction Multiple Data (SIMD), which means that a single instruction can be simultaneously executed in parallel on multiple data streams. GPUs consist of hundreds or thousands of arithmetic-logic units (ALUs), each of which execute a simple instruction set compared to general purpose CPUs, but there is sufficient generality to allow GPUs to be applied to different problem spaces, such as graphics, database processing, image processing... there's actually a rather long list of applications, and AI applications are just the currently hot addition.
Nvidia's GPU development is, IMO, way ahead of anything AMD has or Intel has in that product category. Nvidia has the best GPUs close-coupled with many-core Arm-based server CPUs, and Nvidia has far and away the best system interconnect strategy of these three companies with NVlink and InfiniBand. AMD and Intel, at best, can only apply their coherent CPU interconnects and CXL, which are very limited by comparison. Even CXL, while Intel conceived it as an accelerator to CPU interconnect, has been mostly repositioned to support remote shared memory pools, and does not seemed focused on the CPU-accelerator problem. And on top of all of these advantages, Nvidia's CUDA ecosystem is years ahead of the open software competition (PyTorch).
But there are other strategies for AI processing, that may be better than Nvidia's in the long run.
Google, for several years now, has been using custom ASICs called TPUs (Tensor Processing Units), which are specialized ASICs consisting of multiple matrix-multiply units, each consisting of a 128x128 systolic array capable of 16,384 multiply-accumulate operations per clock cycle in TPU version 4.
Introduction to Cloud TPU | Google Cloud
It's called a Tensor Processing Unit because tensors are mathematical operations from linear algebra used to operate on vectors and matrices, which are the basic data structures in neural networks. In other words, a TPU is a custom hardware implementation of what people are programming GPUs to do. Google assembles multiple TPUs on a board, and then networks thousands of them together on a leading-edge end-to-end optical interconnect called OCS, which incorporates optical switches based on MEMS chips. I think it's amazing. From a technical standpoint, when I read about TPUs and OCS, I'm much more impressed by Google's technology than I am anything Nvidia has. The question is, can Google's AI application technology that runs on these TPU systems be leading edge? I don't know, but they claim to be running various LLMs on these systems already.
TPU v4 enables performance, energy and CO2e efficiency gains | Google Cloud Blog
A new paper describes how Google’s Cloud TPU v4 outperforms TPU v3 by 2.1x on a per-chip basis, and improves performance/Watt by 2.7x.
Amazon has announced they have also developed their own inference ASIC, called Inferentia Accelerators. As you can see from their website, they are already working with customers and have a software ecosystem.
AI Chip - AWS Inferentia - AWS
Learn about AWS Inferentia an ML chip presented by AWS.
So Google and Amazon could win the AI-in-the-cloud market by just being good enough at far lower cost (and likely lower power consumption).
Intel, while it appears to still be working on GPUs, is extending the x86 instruction set for fast matrix extensions, and seems to be working from the bottom up rather than the top down in the AI market:
Intel Xeon is all you need for AI inference: Performance Leadership on Real World Applications
Sanchit Misra is a senior research scientist and leads the efforts in computational biology/HPC research at Intel Labs. Highlights: Intel is democratizing AI inference by delivering a better price and performance for real-world use cases on the 4th gen Intel® Xeon® Scalable Processors...community.intel.com
Tenstorrent appears to have similar ideas to Intel, and is extending RISC-V to better process AI applications.
(The Google-Intel-Tenstorrent situation is somewhat weird, because Dave Patterson, one of the leaders of the RISC-V initiative at UC Berkeley, is a distinguished engineer at Google working on TPUs, while Tenstorrent's CEO, Jim Keller, was previously an SVP at Intel.)
It might be that the CPU-based AI capabilities are just easier to develop applications for, and much cheaper to start small and grow, so this strategy shouldn't be quickly dismissed.
Tenstorrent
Tenstorrent is a next-generation computing company that builds computers for AI. Headquartered in the U.S. with offices in Austin, Texas, and Silicon Valley, and global offices in Toronto, Belgrade, Seoul, Tokyo, and Bangalore, Tenstorrent brings together experts in the field of computer...tenstorrent.com
And then there's Cerebras, a start-up currently valued at $4B+, which uses wafer-scale integration to produce the world's largest monolithic chips. The chips have hundreds of thousands of custom AI processors on each wafer-chip, interconnected by the world's fastest interconnection network (because it's on-die), but they're only available as fully proprietary systems. That means Cerebras is more like the old Cray full-custom supercomputers than the systems Nvidia GPUs are used in, but if we're talking pure technical capability, Cerebras is the current world champion. Why is Cerebras even mentioned in the same breath as Nvidia? Because most of Nvidia's datacenter story is now about assembling GPUs into what are really AI supercomputers,
Cerebras
Cerebras is the go-to platform for fast and effortless AI training. Learn more at www.cerebras.net.
Note the write-up on their website about the creation of a 4 exaflop AI supercomputer.
So I'm not so sure that Nvidia maintains its current far-and-away leadership position in the long run. And it doesn't look like it's AMD that will be the challenger. To me, Google looks most impressive in the long run as a challenger.
Since Cerebras is a private company, outsiders cannot invest yet, but it's my guess that a very large existing public company makes them an acquisition offer they can't refuse. I think probably a systems or cloud computing company. If you look at their "company" web page on their site, their advisors are a who's-who of famous names in the industry. Sam Altman, Andy Bechtolsheim, Nick McKeown, David Perlmutter, Pradeep Sindhu, and Lip-Bu Tan among them. All they're missing is a Kardashian. Dark horses in that acquisition race might be IBM or Oracle someday.
There's a lot more going on in AI chip development than Nvidia and AMD. GPUs happen to be useful for recurrent neural networks, like transformers, which are used by large language model AI software. (You can do a search for all of these terms, and get explanations for what they mean. But keep in mind that understanding the explanations does require some knowledge of moderately advanced computer science concepts.) GPUs are designed to a general computing model called Single Instruction Multiple Data (SIMD), which means that a single instruction can be simultaneously executed in parallel on multiple data streams. GPUs consist of hundreds or thousands of arithmetic-logic units (ALUs), each of which execute a simple instruction set compared to general purpose CPUs, but there is sufficient generality to allow GPUs to be applied to different problem spaces, such as graphics, database processing, image processing... there's actually a rather long list of applications, and AI applications are just the currently hot addition.
Nvidia's GPU development is, IMO, way ahead of anything AMD has or Intel has in that product category. Nvidia has the best GPUs close-coupled with many-core Arm-based server CPUs, and Nvidia has far and away the best system interconnect strategy of these three companies with NVlink and InfiniBand. AMD and Intel, at best, can only apply their coherent CPU interconnects and CXL, which are very limited by comparison. Even CXL, while Intel conceived it as an accelerator to CPU interconnect, has been mostly repositioned to support remote shared memory pools, and does not seemed focused on the CPU-accelerator problem. And on top of all of these advantages, Nvidia's CUDA ecosystem is years ahead of the open software competition (PyTorch).
But there are other strategies for AI processing, that may be better than Nvidia's in the long run.
Google, for several years now, has been using custom ASICs called TPUs (Tensor Processing Units), which are specialized ASICs consisting of multiple matrix-multiply units, each consisting of a 128x128 systolic array capable of 16,384 multiply-accumulate operations per clock cycle in TPU version 4.
Introduction to Cloud TPU | Google Cloud
It's called a Tensor Processing Unit because tensors are mathematical operations from linear algebra used to operate on vectors and matrices, which are the basic data structures in neural networks. In other words, a TPU is a custom hardware implementation of what people are programming GPUs to do. Google assembles multiple TPUs on a board, and then networks thousands of them together on a leading-edge end-to-end optical interconnect called OCS, which incorporates optical switches based on MEMS chips. I think it's amazing. From a technical standpoint, when I read about TPUs and OCS, I'm much more impressed by Google's technology than I am anything Nvidia has. The question is, can Google's AI application technology that runs on these TPU systems be leading edge? I don't know, but they claim to be running various LLMs on these systems already.
TPU v4 enables performance, energy and CO2e efficiency gains | Google Cloud Blog
A new paper describes how Google’s Cloud TPU v4 outperforms TPU v3 by 2.1x on a per-chip basis, and improves performance/Watt by 2.7x.
Amazon has announced they have also developed their own inference ASIC, called Inferentia Accelerators. As you can see from their website, they are already working with customers and have a software ecosystem.
AI Chip - AWS Inferentia - AWS
Learn about AWS Inferentia an ML chip presented by AWS.
So Google and Amazon could win the AI-in-the-cloud market by just being good enough at far lower cost (and likely lower power consumption).
Intel, while it appears to still be working on GPUs, is extending the x86 instruction set for fast matrix extensions, and seems to be working from the bottom up rather than the top down in the AI market:
Intel Xeon is all you need for AI inference: Performance Leadership on Real World Applications
Sanchit Misra is a senior research scientist and leads the efforts in computational biology/HPC research at Intel Labs. Highlights: Intel is democratizing AI inference by delivering a better price and performance for real-world use cases on the 4th gen Intel® Xeon® Scalable Processors...community.intel.com
Tenstorrent appears to have similar ideas to Intel, and is extending RISC-V to better process AI applications.
(The Google-Intel-Tenstorrent situation is somewhat weird, because Dave Patterson, one of the leaders of the RISC-V initiative at UC Berkeley, is a distinguished engineer at Google working on TPUs, while Tenstorrent's CEO, Jim Keller, was previously an SVP at Intel.)
It might be that the CPU-based AI capabilities are just easier to develop applications for, and much cheaper to start small and grow, so this strategy shouldn't be quickly dismissed.
Tenstorrent
Tenstorrent is a next-generation computing company that builds computers for AI. Headquartered in the U.S. with offices in Austin, Texas, and Silicon Valley, and global offices in Toronto, Belgrade, Seoul, Tokyo, and Bangalore, Tenstorrent brings together experts in the field of computer...
And then there's Cerebras, a start-up currently valued at $4B+, which uses wafer-scale integration to produce the world's largest monolithic chips. The chips have hundreds of thousands of custom AI processors on each wafer-chip, interconnected by the world's fastest interconnection network (because it's on-die), but they're only available as fully proprietary systems. That means Cerebras is more like the old Cray full-custom supercomputers than the systems Nvidia GPUs are used in, but if we're talking pure technical capability, Cerebras is the current world champion. Why is Cerebras even mentioned in the same breath as Nvidia? Because most of Nvidia's datacenter story is now about assembling GPUs into what are really AI supercomputers,
Cerebras
Cerebras is the go-to platform for fast and effortless AI training. Learn more at www.cerebras.net.
Note the write-up on their website about the creation of a 4 exaflop AI supercomputer.
So I'm not so sure that Nvidia maintains its current far-and-away leadership position in the long run. And it doesn't look like it's AMD that will be the challenger. To me, Google looks most impressive in the long run as a challenger.
Me too. IBM produces a lot of fascinating research results; I follow IBM Research regularly. But IBM's success in turning that great research into revenue-producing products seems disappointing. It feels like they could do better.
First of all, comparisons between AMD's product that hasn't been delivered in volume yet, and one that has been shipping, like the H100, is what marketing people do, but it's not really an apples to apples comparison. Nvidia recently projected they're going to ship over 500,000 H100s this year alone, so it'll be more interesting to see what Nvidia has on their roadmap. Nvidia also has specialized tensor cores in their GPU design, and perhaps I missed it, but I haven't seen comparable technology in the AMD MI300. Finally, given the significant software development differences between AMD (PyTorch) and Nvidia (CUDA), system deployers are probably buying a roadmap more than an individual product.
Blueone: I appreciate the insights you provide. I have long thought AMD would be NVDA's chief rival in the GPU space. But the insights here about what Google and Amazon are doing has required me to think again and to do more homework. I get the roadmap vs. individual product notion, but I do think AMD could offer a significant savings to potential customers looking to access cutting edge technology at a more affordable price. I assume you don't believe that AMD's so-called matrix cores are anything like an equivalent to NVDA's tensor cores. Thanks for your response.
Ah ha! I thought all along that AMD must have the equivalent of Nvidia's tensor cores in their datacenter GPUs, but AMD's architecture material I was able to find didn't call them out until I knew the magic words to search for - Matrix Cores. So, thanks to you, I found this:
gpuopen.com
Ok. I look forward to hearing your more expert opinion on this issue.Ah ha! I thought all along that AMD must have the equivalent of Nvidia's tensor cores in their datacenter CPUs, but AMD's architecture material I was able to find didn't call them out until I knew the magic words to search for - Matrix Cores. So, thanks to you, I found this:
AMD matrix cores (amd-lab-notes)
This first post in the 'AMD lab notes' series takes a look at AMD’s Matrix Core technology and how best to use it to speed up your matrix operations.
I need some time to study this stuff.
Let's not get carried away. I don't consider myself an expert on this material. My knowledge is relatively high-level, by my standards. But this is a semiconductor forum and AI-ML experts don't naturally hang around here.
Ok, no pressure.
I think you know more about GPUs than you're letting on...Ok, no pressure.
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