> Why aren’t these AI companies submitting to the TOP500 to show off their computing prowess?
my knowledge is 10+ years out of date, but once upon a time if they'd chosen to, Google could have had _several_ entries in the top 10 of the TOP500 list
It's just poker, they didn't want to tip their hand
I’ve worked on several systems that had enough flop/s to make it in the top 5-10, but for which we never submitted benchmarks. Sometimes their backend network layout technically would make them several smaller clusters for an HPL run, sometimes it’s because the cluster is too heterogeneous to get a good benchmark result, and sometimes it’s because the employer wants to keep a low profile.
Most of the time, it just that it’s a hassle. It takes a while to prep and tune a big hero run for benchmarking, and if you spend a billion dollars on a cluster, it’s making you a lot more than that. Taking it down for a day or two stops the money printers.
Yeah, that's true. GPU flops are really impressive in fp16, and more recently fp8 and fp4. When the 40xx GPUs came out, Tim Dettmers had a really cool blog looking into the numbers, and for fp8 one 4090 GPU had enough flops to match the best supercomputer somewhere in the 2000s, with an 8x 4090 build being top for a few years more. It's insane. But it gets nowhere close on fp64, where most of the physics simulations and other usual supercomputer tasks shine.
My sense is you only submit if you are in the business of selling supercomputing cluster (IBM, Cray). If you are a consumer or build to consume internally, you would care less.
Is there international value to these designations? As in, would it be worth it for the U.S. to pay a bonus to anyone who qualifies into the TOP500, to offset the cost of the run?
Most of the US systems in the TOP500 are funded by the US government. It isn’t considered a meaningful demonstration of capability by most people in the know.
Even before AI training clusters became important, Google has had an outstanding custom fabric (there's papers about it) together with the ability to tune NICs for their own cases, and "their own cases" meant nearly everything engineered within Google. Ethernet hardware has had low kernel latency and DMA for a long time; it's the rest of the stack that hurts. But as far back as the early 2010s (if not further back, that goes beyond my knowledge horizon), you could just make it not hurt, if you had the software engineers to do it.
All that would not help you with an AI training cluster interconnect. See Amin Vahdat's keynote at HotInterconnects 2025. Everyone is building a fabric for this stuff from scratch (Google/Falcon, Amazon/EFA, Azure/MANA, Cornelis/CN5000, and obviously Mellanox).
Historically there have been a bunch of clusters on the Top 500 that weren't used for HPC. The tell is that they used Ethernet (this was before RoCE). It's less efficient but you can still get an OK Linpack score.
Why would the scientific computing people want to tip their hand? It’s an open secret that the main point of these mammoth FP64 compute machines is to simulate nuclear weapons detonations to comply with the CTBT you’d think that crowd would really not be fans of broadcasting their capabilities.
In adversarial scenarios, there are varying strategies in communicating one's capabilities, just as one might do in a poker game.
Sometimes you want to show off what you can do to dissuade others from fucking with you. Sometimes you want to undersell your capabilities to hide your true ability. Sometimes you want others to think you are underselling your capabilities when you are actually at a disadvantage.
It is partly this and partly a funding vehicle for American next-gen computing. It is not that hard to estimate FP64 ballpark from a whole bunch of public statistics. And it takes a looot more than raw FLOPs to get a simulation working. And presumably a looot more to translate it into practice. And the openness makes it easier to talk to different vendors and not get in the way of them having all the H1Bs it takes to get these things to work.
Plus one think I like to say is that if a bullet is flying towards you, you could know everything about the chemistry of the gunpowder and the composition of the alloy without it affecting what happens next.
What for? You only need to match the performance that existed in the 1950s. In the Soviet Union. Everything else is a lack of knowledge rather than computing power.
Also you should read the second sentence of the CTBT Wikipedia article to find out why it's not even in force (spoiler: US hasn't ratified it).
At some point, you will get diminishing returns no? I don't think compute is the bottleneck right now for mechanical engineering if you don't count AI.
TOP500 hasn't been a particularly useful measure of practical computing power in modern systems for many years because what it measures isn't a significant bottleneck in most real systems. It has become a measure of how much money someone is willing to spend for bragging rights. (HPCG is better in that it is a bit more bandwidth focused but still pretty narrow.)
Most companies with huge systems don't participate.
The new Chinese supercomputer beats all US supercomputers also in HPCG, not only in Linpack.
What is remarkable is that this was done despite the US attempts of sabotaging HPC in China by "sanctions".
This uses custom CPUs designed in China, which implement an Armv9-A ISA with SME (scalable matrix extension) and which use fast HBM memory. These CPUs are fast enough that they do not need any GPUs for exceeding the throughput of the American supercomputers, which use GPUs. This is like in the Japanese Fugaku, which was the first to implement the Armv8-A ISA with SVE, but which now is rather old.
Like in all CPU-based supercomputers, for this new Chinese supercomputer it is much easier to reach a higher percentage of the theoretical maximum throughput, when solving any problem. So for most practical problems it will be faster than a GPU-based supercomputer that would have the same theoretical maximum throughput.
So this is a much more interesting supercomputer than those built by just buying some HPC racks from HPE (Cray). Because China was forbidden to buy the American equipment, they had to innovate and design their own. Eventually they made something better than what they could not buy.
It isn’t “sour grapes”, I remember when the HPC community largely abandoned these benchmarks two decades ago because they weren’t representative of anything real for most of them. The benchmark is a poor reflection of real workloads. There was a long period when the STREAM benchmark was the primary correlate with real-world performance for most HPC workloads but you can’t build a press release from that.
I don’t have a dog in this fight and I no longer work in HPC. Most modern workloads are severely bandwidth bound. The only aspect of the hardware that matters is bandwidth and that is not materially differentiated. The frontier is scheduler design, which is pure software and difficult computer science. HPC competitions avoid problems with a software solution because it isn’t in their interest as hardware manufacturers.
This result is impressive, sort of, but not in the way people are imagining. I was equally dismissive of the previous leader for the same reasons. For most applications, these benchmarks are legacy pagentry.
As I have mentioned, and as described in TFA, this supercomputer is also leading in memory bandwidth (4 TB/s per socket => correction, it is 8 Tb/s per socket).
I agree with what you say about benchmarks, but that is precisely why the advantage of this supercomputer over the following American supercomputers will be even greater in more demanding workloads than Linpack and HPCG.
It has already shown this by having an advantage in HPCG of greater than 26% over the fastest US system, while in Linpack its advantage is of only 22%.
Thus its position in the top cannot be dismissed as insignificant, because it more likely underestimates than overestimates this system.
Also the programming effort for writing an efficient program will be lower than for the GPU-based US supercomputers.
I definitely appreciate the CPU-centric approach. It appeals to my biases and aligns with my technical perspective.
The memory bandwidth is something you can buy. Exotics were >1 TB/s over a decade ago, so 4 TB/s in 2026 is not that impressive. For all practical purposes, these CPUs are also still exotics, you can’t just buy them. I would be very surprised if the memory bandwidth of US exotics haven’t improved over the last 10-15 years.
In any case, for real workloads scalability is mostly a software theory problem at this point and that is still a dark art without much literature.
Correction, I took the 4 TB/s from TFA, but the NextPlatform article clarifies that it is 4 TB/s per chiplet, but 8 TB/s per socket, so more impressive.
The only US-designed CPU "exotics" are the Intel Xeon Max CPU series, which use HBM like the Chinese CPUs, but which have a theoretical maximum throughput of only 1.6 TB/s per socket, i.e. 5 times slower than the new Chinese CPUs.
Moreover, the users of Intel Xeon Max complained that they cannot reach the theoretical memory bandwidth. I do not know if that was due to some bug that might have been solved later by Intel with a microcode update or a new mask set stepping.
The server CPUs with standard DIMMs, which will be launched by AMD and Intel next year, will have a memory bandwidth of around 1 TB/s per socket.
The AMD MI300 GPU used in the fastest US supercomputer has a memory throughput of 5.2 TB/s per socket, so lower than the 8 TB/s per socket of the Chinese CPU, which explains why the advantage of the Chinese system increases in the benchmarks more dependent on memory performance.
The latest AMD Instinct GPU, MI355X, increases the memory bandwidth to 8 TB/s, so equal to the Chinese CPU.
However, it may pass some time until someone will build such a big system with MI355X, though perhaps the existence of this new contender might prompt the US labs to upgrade their systems by replacing the older AMD GPUs with newer AMD GPUs.
I wonder if there would have been an opportunity to generate some finer-grained benchmarks with something like BiCGStab+ILU (or maybe CG+incomplete cholesky). Instead of CG+Gauss Seidel. The pitch being, you might have made different memory vs compute trade-offs with designing your cluster, but you should be able to select a fill-in factor for the preconditioner to suit it.
I think you could build more representative benchmarks that capture capability better. The tension is that HPC companies are pure hardware companies and you need a lot of help from software to make your hardware look good. They don’t like that. Most of our software sucks at the scales they try to benchmark.
Ironically, the related Graph500 benchmarks reflect this better. Performance is dependent more on using the hardware better than better hardware per se.
> We think it is highly likely that these LX2 chiplets are etched using SMIC 7 nanometer processes at the N+3 refinement, and we base that on the fact that the chip only runs at 1.55 GHz. That is nowhere near the 3 GHz that SMIC can push with that process, but it is probably lower to get the memory and core speeds more balanced. [1]
Despite what it says at that link, it is more likely to be based on Armv9.3-A ISA, because it supports SME.
In the CPU cores designed by the Arm company, SME has been added only in the latest generation of Armv9.3-A CPUs, which was launched last year.
For each level of Armv9, there are many mandatory features and many optional features.
If the Chinese CPU does not implement all the mandatory Armv9.3-A features (and we do not know anything about this), then it will still be considered only an Armv9.2-A CPU, but even in that case it should be referred as an Armv9.2-A + SME, in order to not confuse it with the Armv9.2-A CPUs that have been used for a few years in smartphones, laptops and mini-PCs and which do not have SME, so they cannot have a comparable performance.
I haven't kept up with the latest on supercomputing power, but I recall some years ago there being strong evidence that China had a couple of un-announced supercomputers that would have topped the charts. It makes me wonder what is publicly disclosed vs. actual.
Interesting to see PAC mentioned on the slide, I'd have assumed security features would be a waste of transistors on something so compute-optimized - but maybe they want to isolate workloads from each other?
Yes exactly. it means you can sell isolated work units. With cloud being, well, cloud, if you can do that, you then have a market, with bidders able to bid higher for more capacity right now, vs a lower bid for eventually.
TOP500 can be done with inexpensive silicon. It is more about a willingness to aggregate enough hardware in one place. As a benchmark, it tells you almost nothing about computing power or scalability for other applications because it doesn't exercise the bottlenecks most high-scale applications have.
Didn't the DoD at one point build a 1k+ PS3 cluster based on their multi-core chip and had a mini supercomputer CotS?
I remember Sony not liking that people were buying them for other things rather than gaming (iirc they were losing money on hardware at the time) so they bricked linux support soon after.
The last time when China had the fastest supercomputer, it was more than 20 times slower than this one and more than 8 times less efficient in energy consumption.
Moreover, its capability was overestimated by the Linpack benchmarks and in other workloads its performance was much less impressive.
For this system, it is the opposite situation. Its result in Top500 underestimates it capability. In other more demanding workloads, where the influence of the memory bandwidth and latency is stronger, its advantage over the US supercomputers is greater than in Top500.
We're too busy regulating the tech, not granting access to US engineers and companies, arguing against power and data centers, stopping skilled immigration.
This is absolutely going to bite us in the face in five to ten years.
Separate issue that has nothing to do with US manufacturing or HPC. I think our retreat from science funding and offshoring advanced manufacturing is a bigger issue.
It is likely that those cores are dedicated to unrelated management, monitoring, and administrative tasks. This is common and many workloads are throttled on bandwidth anyway. For the purposes of the benchmark, those cores are not participating in the workload.
And then even if some nodes had 40/40 "good" cores, it would make load balancing a lot more complicated if core counts vary. Easier to turn them off at the hardware level.
Why are they not using GPUs? is it use cases that don't suit GPUs or because of the limitations they are imposing on themselves to use SMIC domestic chips?
The reason is that with GPUs it is far more difficult to reach a great percentage of the maximum theoretical throughput. Most GPU programs reach only a very small fraction of what is theoretically possible, and in the best cases one may reach something like 50% to 60% of the maximum.
This CPU-based supercomputer has demonstrated reaching 80% of the theoretical maximum throughput, and this is typical for CPU-based supercomputers. It is much easier to write efficient programs for CPUs.
The new custom Chinese CPUs, which use SME, the Arm Scalable Matrix Extension, are fast enough that they have beaten all GPU-based supercomputers, so there was no need to use GPUs.
Moreover these CPUs use HBM for a very fast memory interface, so in the benchmarks that depend more on memory bandwidth they have an even greater advance over the US GPU-based supercomputers. Thus there really was no point in using GPUs.
GPUs are necessary only when your CPUs are not good enough, which was not the case here.
In the recent past, the Japanese Fugaku used the same approach, of avoiding GPUs. At that time, their custom CPUs using the Armv8-A ISA with SVE were the first which used this ISA in HPC, but now that ISA variant is obsolete in comparison with the Armv9-A ISA with SME, which is implemented in these new custom Chinese CPUs.
I’m not sure if I’m missing a joke, but that’s why we have general purpose computing on graphics processing units (GPGPU) which is why 8/10 of the top 10 machines have GPUs.
GPUs were for graphics. Now, they're mainly used for machine learning training and inference. The big tech companies are spending eye-watering amounts for GPUs - hundreds of billions of dollars a year each. That's the reason that Nvidia's market cap is at $4.66 trillion.
The AI oriented GPUs or TPUs have either weak FP64 throughput or they may not support FP64 at all.
They can compete neither with CPUs nor with GPUs that have good FP64 support, like the AMD CDNA datacenter GPUs, which occupy all the top places among American supercomputers.
NVIDIA has stopped improving the FP64 throughput even in their "datacenter" GPUs, abandoning this nowadays smaller market to AMD.
The AMD CDNA GPUs can be used for both HPC and AI, so only an AI cluster based on them could have dual use, but most who want AI choose NVIDIA.
Most claims about the cost of emulating FP64 on GPUs are wrong, because they assume that only the significand of floating-point numbers must be extended.
In reality it is even more important to extend the exponent, because with the exponent of FP32 overflows would be much too frequent in scientific/technical computations to accomplish anything.
The minimum FP64 emulation on FP32-capable GPUs requires 3 numbers per emulated FP64, which may be 3 FP32 numbers, or the exponent may be an Int32, if that works better on the target GPU. An emulated FP64 operation is likely to be at least 20 times slower than a FP32 operation.
That is much faster than the 1:64 ratio provided in hardware by an NVIDIA GPU, but even on the fastest FP32 GPUs it is too slow to compete with CPUs, in a professional setting.
FP64 emulation on a GPU can be useful only in a home computer, which may have a rather weak CPU and increasing the FP64 throughput using the GPU can be done at no additional cost, so it can be worthwhile.
> Why aren’t these AI companies submitting to the TOP500 to show off their computing prowess?
my knowledge is 10+ years out of date, but once upon a time if they'd chosen to, Google could have had _several_ entries in the top 10 of the TOP500 list
It's just poker, they didn't want to tip their hand
I’ve worked on several systems that had enough flop/s to make it in the top 5-10, but for which we never submitted benchmarks. Sometimes their backend network layout technically would make them several smaller clusters for an HPL run, sometimes it’s because the cluster is too heterogeneous to get a good benchmark result, and sometimes it’s because the employer wants to keep a low profile.
Most of the time, it just that it’s a hassle. It takes a while to prep and tune a big hero run for benchmarking, and if you spend a billion dollars on a cluster, it’s making you a lot more than that. Taking it down for a day or two stops the money printers.
What programs were yours running to print money?
Also, would those 550k Blackwell have good FP64 performance? How would one even compare them?
Yeah, that's true. GPU flops are really impressive in fp16, and more recently fp8 and fp4. When the 40xx GPUs came out, Tim Dettmers had a really cool blog looking into the numbers, and for fp8 one 4090 GPU had enough flops to match the best supercomputer somewhere in the 2000s, with an 8x 4090 build being top for a few years more. It's insane. But it gets nowhere close on fp64, where most of the physics simulations and other usual supercomputer tasks shine.
My sense is you only submit if you are in the business of selling supercomputing cluster (IBM, Cray). If you are a consumer or build to consume internally, you would care less.
Is there international value to these designations? As in, would it be worth it for the U.S. to pay a bonus to anyone who qualifies into the TOP500, to offset the cost of the run?
Most of the US systems in the TOP500 are funded by the US government. It isn’t considered a meaningful demonstration of capability by most people in the know.
Cloud computing is not a supercomputer. Different architecture, bandwitch, interconnectivity and latencies.
That's not nearly as true when you look at AI training clusters. They're basically supercomputers but without an FP64 focus.
(These are the systems to which GP was referring at Google.)
Even before AI training clusters became important, Google has had an outstanding custom fabric (there's papers about it) together with the ability to tune NICs for their own cases, and "their own cases" meant nearly everything engineered within Google. Ethernet hardware has had low kernel latency and DMA for a long time; it's the rest of the stack that hurts. But as far back as the early 2010s (if not further back, that goes beyond my knowledge horizon), you could just make it not hurt, if you had the software engineers to do it.
All that would not help you with an AI training cluster interconnect. See Amin Vahdat's keynote at HotInterconnects 2025. Everyone is building a fabric for this stuff from scratch (Google/Falcon, Amazon/EFA, Azure/MANA, Cornelis/CN5000, and obviously Mellanox).
I thought TPUs couldn't reasonably run LINPACK at all because TPUs do not acknowledge that FP64 exists.
I know Google wants to compare their stuff to El Capitan or whatever but the comparison does not seem valid to me.
Historically there have been a bunch of clusters on the Top 500 that weren't used for HPC. The tell is that they used Ethernet (this was before RoCE). It's less efficient but you can still get an OK Linpack score.
Why would the scientific computing people want to tip their hand? It’s an open secret that the main point of these mammoth FP64 compute machines is to simulate nuclear weapons detonations to comply with the CTBT you’d think that crowd would really not be fans of broadcasting their capabilities.
In adversarial scenarios, there are varying strategies in communicating one's capabilities, just as one might do in a poker game.
Sometimes you want to show off what you can do to dissuade others from fucking with you. Sometimes you want to undersell your capabilities to hide your true ability. Sometimes you want others to think you are underselling your capabilities when you are actually at a disadvantage.
It is partly this and partly a funding vehicle for American next-gen computing. It is not that hard to estimate FP64 ballpark from a whole bunch of public statistics. And it takes a looot more than raw FLOPs to get a simulation working. And presumably a looot more to translate it into practice. And the openness makes it easier to talk to different vendors and not get in the way of them having all the H1Bs it takes to get these things to work.
Plus one think I like to say is that if a bullet is flying towards you, you could know everything about the chemistry of the gunpowder and the composition of the alloy without it affecting what happens next.
What for? You only need to match the performance that existed in the 1950s. In the Soviet Union. Everything else is a lack of knowledge rather than computing power.
Also you should read the second sentence of the CTBT Wikipedia article to find out why it's not even in force (spoiler: US hasn't ratified it).
At some point, you will get diminishing returns no? I don't think compute is the bottleneck right now for mechanical engineering if you don't count AI.
TOP500 hasn't been a particularly useful measure of practical computing power in modern systems for many years because what it measures isn't a significant bottleneck in most real systems. It has become a measure of how much money someone is willing to spend for bragging rights. (HPCG is better in that it is a bit more bandwidth focused but still pretty narrow.)
Most companies with huge systems don't participate.
This seems like a "sour grapes" comment.
The new Chinese supercomputer beats all US supercomputers also in HPCG, not only in Linpack.
What is remarkable is that this was done despite the US attempts of sabotaging HPC in China by "sanctions".
This uses custom CPUs designed in China, which implement an Armv9-A ISA with SME (scalable matrix extension) and which use fast HBM memory. These CPUs are fast enough that they do not need any GPUs for exceeding the throughput of the American supercomputers, which use GPUs. This is like in the Japanese Fugaku, which was the first to implement the Armv8-A ISA with SVE, but which now is rather old.
Like in all CPU-based supercomputers, for this new Chinese supercomputer it is much easier to reach a higher percentage of the theoretical maximum throughput, when solving any problem. So for most practical problems it will be faster than a GPU-based supercomputer that would have the same theoretical maximum throughput.
So this is a much more interesting supercomputer than those built by just buying some HPC racks from HPE (Cray). Because China was forbidden to buy the American equipment, they had to innovate and design their own. Eventually they made something better than what they could not buy.
It isn’t “sour grapes”, I remember when the HPC community largely abandoned these benchmarks two decades ago because they weren’t representative of anything real for most of them. The benchmark is a poor reflection of real workloads. There was a long period when the STREAM benchmark was the primary correlate with real-world performance for most HPC workloads but you can’t build a press release from that.
I don’t have a dog in this fight and I no longer work in HPC. Most modern workloads are severely bandwidth bound. The only aspect of the hardware that matters is bandwidth and that is not materially differentiated. The frontier is scheduler design, which is pure software and difficult computer science. HPC competitions avoid problems with a software solution because it isn’t in their interest as hardware manufacturers.
This result is impressive, sort of, but not in the way people are imagining. I was equally dismissive of the previous leader for the same reasons. For most applications, these benchmarks are legacy pagentry.
As I have mentioned, and as described in TFA, this supercomputer is also leading in memory bandwidth (4 TB/s per socket => correction, it is 8 Tb/s per socket).
I agree with what you say about benchmarks, but that is precisely why the advantage of this supercomputer over the following American supercomputers will be even greater in more demanding workloads than Linpack and HPCG.
It has already shown this by having an advantage in HPCG of greater than 26% over the fastest US system, while in Linpack its advantage is of only 22%.
Thus its position in the top cannot be dismissed as insignificant, because it more likely underestimates than overestimates this system.
Also the programming effort for writing an efficient program will be lower than for the GPU-based US supercomputers.
I definitely appreciate the CPU-centric approach. It appeals to my biases and aligns with my technical perspective.
The memory bandwidth is something you can buy. Exotics were >1 TB/s over a decade ago, so 4 TB/s in 2026 is not that impressive. For all practical purposes, these CPUs are also still exotics, you can’t just buy them. I would be very surprised if the memory bandwidth of US exotics haven’t improved over the last 10-15 years.
In any case, for real workloads scalability is mostly a software theory problem at this point and that is still a dark art without much literature.
Correction, I took the 4 TB/s from TFA, but the NextPlatform article clarifies that it is 4 TB/s per chiplet, but 8 TB/s per socket, so more impressive.
The only US-designed CPU "exotics" are the Intel Xeon Max CPU series, which use HBM like the Chinese CPUs, but which have a theoretical maximum throughput of only 1.6 TB/s per socket, i.e. 5 times slower than the new Chinese CPUs.
Moreover, the users of Intel Xeon Max complained that they cannot reach the theoretical memory bandwidth. I do not know if that was due to some bug that might have been solved later by Intel with a microcode update or a new mask set stepping.
The server CPUs with standard DIMMs, which will be launched by AMD and Intel next year, will have a memory bandwidth of around 1 TB/s per socket.
The AMD MI300 GPU used in the fastest US supercomputer has a memory throughput of 5.2 TB/s per socket, so lower than the 8 TB/s per socket of the Chinese CPU, which explains why the advantage of the Chinese system increases in the benchmarks more dependent on memory performance.
The latest AMD Instinct GPU, MI355X, increases the memory bandwidth to 8 TB/s, so equal to the Chinese CPU.
However, it may pass some time until someone will build such a big system with MI355X, though perhaps the existence of this new contender might prompt the US labs to upgrade their systems by replacing the older AMD GPUs with newer AMD GPUs.
I wonder if there would have been an opportunity to generate some finer-grained benchmarks with something like BiCGStab+ILU (or maybe CG+incomplete cholesky). Instead of CG+Gauss Seidel. The pitch being, you might have made different memory vs compute trade-offs with designing your cluster, but you should be able to select a fill-in factor for the preconditioner to suit it.
I think you could build more representative benchmarks that capture capability better. The tension is that HPC companies are pure hardware companies and you need a lot of help from software to make your hardware look good. They don’t like that. Most of our software sucks at the scales they try to benchmark.
Ironically, the related Graph500 benchmarks reflect this better. Performance is dependent more on using the hardware better than better hardware per se.
Additional details in:
https://arxiv.org/abs/2605.08633v1
https://www.servethehome.com/arm-cpus-take-number-1-in-lates...
https://www.top500.org/news/lineshine-debuts-no-1-top500-ent...
> We think it is highly likely that these LX2 chiplets are etched using SMIC 7 nanometer processes at the N+3 refinement, and we base that on the fact that the chip only runs at 1.55 GHz. That is nowhere near the 3 GHz that SMIC can push with that process, but it is probably lower to get the memory and core speeds more balanced. [1]
Based on the ARMv9.2.
[1] https://www.nextplatform.com/hpc/2026/06/25/a-deep-dive-on-c...
Despite what it says at that link, it is more likely to be based on Armv9.3-A ISA, because it supports SME.
In the CPU cores designed by the Arm company, SME has been added only in the latest generation of Armv9.3-A CPUs, which was launched last year.
For each level of Armv9, there are many mandatory features and many optional features.
If the Chinese CPU does not implement all the mandatory Armv9.3-A features (and we do not know anything about this), then it will still be considered only an Armv9.2-A CPU, but even in that case it should be referred as an Armv9.2-A + SME, in order to not confuse it with the Armv9.2-A CPUs that have been used for a few years in smartphones, laptops and mini-PCs and which do not have SME, so they cannot have a comparable performance.
I haven't kept up with the latest on supercomputing power, but I recall some years ago there being strong evidence that China had a couple of un-announced supercomputers that would have topped the charts. It makes me wonder what is publicly disclosed vs. actual.
Interesting to see PAC mentioned on the slide, I'd have assumed security features would be a waste of transistors on something so compute-optimized - but maybe they want to isolate workloads from each other?
Yes exactly. it means you can sell isolated work units. With cloud being, well, cloud, if you can do that, you then have a market, with bidders able to bid higher for more capacity right now, vs a lower bid for eventually.
You can see the list yourself at https://top500.org/
Deep link: https://top500.org/lists/top500/list/2026/06/
Extremely impressive accomplishment considering they did this with Chinese interconnects and Chinese chips. This is a wake up call.
TOP500 can be done with inexpensive silicon. It is more about a willingness to aggregate enough hardware in one place. As a benchmark, it tells you almost nothing about computing power or scalability for other applications because it doesn't exercise the bottlenecks most high-scale applications have.
> TOP500 can be done with inexpensive silicon.
Didn't the DoD at one point build a 1k+ PS3 cluster based on their multi-core chip and had a mini supercomputer CotS?
I remember Sony not liking that people were buying them for other things rather than gaming (iirc they were losing money on hardware at the time) so they bricked linux support soon after.
> Didn't the DoD at one point build a 1k+ PS3 cluster based on their multi-core chip and had a mini supercomputer CotS?
The Air Force did (Condor) and it hit #33 on the 2010 Top500.
Not the first time that happened, even for China: https://en.wikipedia.org/wiki/List_of_fastest_computers
This is quite different.
The last time when China had the fastest supercomputer, it was more than 20 times slower than this one and more than 8 times less efficient in energy consumption.
Moreover, its capability was overestimated by the Linpack benchmarks and in other workloads its performance was much less impressive.
For this system, it is the opposite situation. Its result in Top500 underestimates it capability. In other more demanding workloads, where the influence of the memory bandwidth and latency is stronger, its advantage over the US supercomputers is greater than in Top500.
[delayed]
We're too busy regulating the tech, not granting access to US engineers and companies, arguing against power and data centers, stopping skilled immigration.
This is absolutely going to bite us in the face in five to ten years.
Separate issue that has nothing to do with US manufacturing or HPC. I think our retreat from science funding and offshoring advanced manufacturing is a bigger issue.
> Two cores are disabled per cluster.
I’m sure there is a good reason for this, which is..?
It is likely that those cores are dedicated to unrelated management, monitoring, and administrative tasks. This is common and many workloads are throttled on bandwidth anyway. For the purposes of the benchmark, those cores are not participating in the workload.
Yield. Some fraction of cores had a speck of dust or something, but at 38/40 good cores per chip they got economical yield
And then even if some nodes had 40/40 "good" cores, it would make load balancing a lot more complicated if core counts vary. Easier to turn them off at the hardware level.
Couldn't some chips have 40 good cores, while others have only 36? Do they all need to be exactly 38?
Why are they not using GPUs? is it use cases that don't suit GPUs or because of the limitations they are imposing on themselves to use SMIC domestic chips?
If you can avoid GPUs, that is preferable.
The reason is that with GPUs it is far more difficult to reach a great percentage of the maximum theoretical throughput. Most GPU programs reach only a very small fraction of what is theoretically possible, and in the best cases one may reach something like 50% to 60% of the maximum.
This CPU-based supercomputer has demonstrated reaching 80% of the theoretical maximum throughput, and this is typical for CPU-based supercomputers. It is much easier to write efficient programs for CPUs.
The new custom Chinese CPUs, which use SME, the Arm Scalable Matrix Extension, are fast enough that they have beaten all GPU-based supercomputers, so there was no need to use GPUs.
Moreover these CPUs use HBM for a very fast memory interface, so in the benchmarks that depend more on memory bandwidth they have an even greater advance over the US GPU-based supercomputers. Thus there really was no point in using GPUs.
GPUs are necessary only when your CPUs are not good enough, which was not the case here.
In the recent past, the Japanese Fugaku used the same approach, of avoiding GPUs. At that time, their custom CPUs using the Armv8-A ISA with SVE were the first which used this ISA in HPC, but now that ISA variant is obsolete in comparison with the Armv9-A ISA with SME, which is implemented in these new custom Chinese CPUs.
I suspect Chinese GPUs (e.g. Biren) are not mature.
GPUs are for graphics (the G in GPU). These systems are used for more general computations.
I’m not sure if I’m missing a joke, but that’s why we have general purpose computing on graphics processing units (GPGPU) which is why 8/10 of the top 10 machines have GPUs.
GPUs were for graphics. Now, they're mainly used for machine learning training and inference. The big tech companies are spending eye-watering amounts for GPUs - hundreds of billions of dollars a year each. That's the reason that Nvidia's market cap is at $4.66 trillion.
In between those, they were for Ethereum mining.
Previously on HN: https://news.ycombinator.com/item?id=48658334
No comments there though.
Would the AI “GW-scale” clusters be able to run the Top500 benchmarks meaningfully? And what might be the outcome?
No.
The AI oriented GPUs or TPUs have either weak FP64 throughput or they may not support FP64 at all.
They can compete neither with CPUs nor with GPUs that have good FP64 support, like the AMD CDNA datacenter GPUs, which occupy all the top places among American supercomputers.
NVIDIA has stopped improving the FP64 throughput even in their "datacenter" GPUs, abandoning this nowadays smaller market to AMD.
The AMD CDNA GPUs can be used for both HPC and AI, so only an AI cluster based on them could have dual use, but most who want AI choose NVIDIA.
> And what might be the outcome?
DoE compute budgets are ~10B USD across labs. AI training is a trillion-dollar workload. Different league.
Yes, they should score well on Linpack as long as they use Ozaki emulation.
No, that is too slow.
Most claims about the cost of emulating FP64 on GPUs are wrong, because they assume that only the significand of floating-point numbers must be extended.
In reality it is even more important to extend the exponent, because with the exponent of FP32 overflows would be much too frequent in scientific/technical computations to accomplish anything.
The minimum FP64 emulation on FP32-capable GPUs requires 3 numbers per emulated FP64, which may be 3 FP32 numbers, or the exponent may be an Int32, if that works better on the target GPU. An emulated FP64 operation is likely to be at least 20 times slower than a FP32 operation.
That is much faster than the 1:64 ratio provided in hardware by an NVIDIA GPU, but even on the fastest FP32 GPUs it is too slow to compete with CPUs, in a professional setting.
FP64 emulation on a GPU can be useful only in a home computer, which may have a rather weak CPU and increasing the FP64 throughput using the GPU can be done at no additional cost, so it can be worthwhile.
Just glad to see Hamburg mentioned :) Hope you all didn’t suffer too much through the current heatwave
Is it the first to reach 2 exaflops?
Yes.
The fastest US supercomputer, El Capitan at Lawrence Livermore National Laboratory, reaches only 1.809 exaflops, while this reaches 2.198, 22% higher.
On the HPCG benchmark, which is more strongly influenced by memory bandwidth, the advance over the GPU-based El Capitan is even greater, of 26.4%.
How many tokens/s? :)
And you know it needs a well-conceived OS to be able to run it: it's got to be... Microsoft Windows right?
The OS powering 0% of the 500 supercomputers of the Top 500. But this time, it has to be Windows, right? Amirite?
Ah, no, just kidding: it's "Kylin OS". It used to be a BSD derivative and now it's just based on Linux.
I know, I know: "It's a heavily modified Linux". Whatever, it's not Windows and that makes me very happy.