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93 points rbanffy | 8 comments | | HN request time: 0s | source | bottom
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pama ◴[] No.42188372[source]
Noting here that 2700 quadrillion operations per second is less than the estimated sustained throughput of productive bfloat16 compute during the training of the large llama3 models, which IIRC was about 45% of 16,000 quadrillion operations per second, ie 16k H100 in parallel at about 0.45 MFU. The compute power of national labs has fallen far behind industry in recent years.
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1. alephnerd ◴[] No.42188389[source]
Training an LLM (basically Transformers) is different workflow from Nuclear Simulations (basically Monte Carlo simulations)

There are a lot of intricates, but at a high level they require different compute approaches.

replies(3): >>42188413 #>>42188417 #>>42188497 #
2. handfuloflight ◴[] No.42188413[source]
Can you expand on why the operations per second is not an apt comparison?
replies(1): >>42188538 #
3. pama ◴[] No.42188417[source]
Absolutely. Though the performance of El Capitain is only measured by a linpack benchmark not the actual application.
replies(1): >>42188515 #
4. Koshkin ◴[] No.42188497[source]
This is about the raw compute, no matter the workflow.
replies(1): >>42193796 #
5. pertymcpert ◴[] No.42188515[source]
I thought modern supercomputers use benchmarks like HPCG instead of LINPACK?
replies(1): >>42188963 #
6. pertymcpert ◴[] No.42188538[source]
When you're doing scientific simulations, you're generally a lot more sensitive to FP precision than ML training which is very, very tolerant of reduced precision. So while FP8 might be fine for transformer networks, it would likely be unacceptably inaccurate/unusable for simulations.
7. fancyfredbot ◴[] No.42188963{3}[source]
The top 500 includes both. There is no HPCG result for El Capitan yet:

https://top500.org/lists/hpcg/2024/11/

8. alephnerd ◴[] No.42193796[source]
It isn't. I recommend reading u/pertymcpert's response.