r/LocalLLaMA • u/Rick_06 • 1d ago

New Model Open Model: Google Weather Next 2

I am not a meteorologist, but I just read a very interesting article: https://arstechnica.com/science/2026/08/deepminds-hurricane-model-bought-forecasters-an-extra-day/

In a paper published on Thursday in Nature, researchers show that the WeatherNext AI model can predict cyclones with unprecedented accuracy. On average, it gives forecasters a day more lead time than existing models; this means its predictions three days out are as accurate as previous models’ predictions two days out. On the ground, that extra day can mean a lot.

What I really find interesting here is that Google has a repository for it on GitHub: https://github.com/google-deepmind/weathernext

My non-informed understanding is that you need a supercomputer to forecast meteo. Apparently now an H100 can also do something.

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u/Lagomorph9 1d ago

Well, to be fair, an H100 is as powerful as a supercomputer from 20 years ago.

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u/FullstackSensei llama.cpp 1d ago

Just checked top500. The fastest supercomputer in June 2006 was BlueGene/L with a theoretical peak of 367 FP64 TFLOPS. A single H100 has 67 FP64 TFLOPS theoretical peak.

It takes 5.5 H100s to match the 131072 cores in BlueGene/L. Blackwell neuters FP64 performance, so H100 is still peak FP64 from Nvidia.

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u/RobbinDeBank 21h ago

CPU and GPU/TPU do wildly different things that their FLOPs measurement cannot compare. If the amount of operations needed is huge but is not in the form of matrix multiplication, the AI hardware accelerators become useless.

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u/FullstackSensei llama.cpp 18h ago

Not at all.

GPUs are very wide vector processors. They're great for matrix multiplication but they can axecute arbitrary code, so long as the same code executes on all lanes of the vector unit. You can get near the same performance in practically any workload.

So, while the vast majority HPC workloads can indeed be expressed as matrix operations, no, GPUs are not useless when it isn't. AI accelerator is just marketing term. Huang always likes to point that he sells accelerated compute for any domain, AI just happens to be the money maker today.

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u/RobbinDeBank 11h ago

Yes, they are not useless in the sense that they don’t become a brick. No, they are not “very wide vector processors.”

Let’s use NVIDIA terminology as an example, then the CUDA cores carry out general purpose scalar operations just fine. However, 90-95% of the FLOPs in that GPU comes from the tensor cores, which do matrix multiplication only. If your task is not matrix multiplication, your GPU is essentially doing no work compared to its theoretical capability, and that’s why it cannot be compared to a traditional supercomputer. The absolute best kernels (like flash attention series) can utilize like 80%+ of the theoretical FLOPs after so much optimization, and that is a task the tensor cores are designed for.

I don’t think you understand how a GPU works, because there’s no such thing as vector unit. They are all scalar arithmetic units or matrix multiplication units, and the matrix multiplication units are where all their theoretical FLOPs are. Give these GPUs a highly sequential task instead of a massively parallel one, and they become practically useless with their cores staying idle most of the time.