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Re:DeepX claims inference up to 100B parameters at less than... by augment_me on Monday November 09, 2026 @02:11PM (#50430162)
Quirks from the article:

1) The performance numbers are simulated

2) The details say that the performance numbers require 2.7x the overall power, so around 13.5W. Power needed for all the memory is not explicit in the numbers.

3) The compiler is also simulated and it's a prototype, so it's not a drop-in replacement for anything like Nvidia ISAAC

4) 5W seems to refer to sub-4B models, so not the 100B param ones.

Re:DeepX claims inference up to 100B parameters at less than... by idiotsecant on Monday November 09, 2026 @03:09PM (#50431219)
I think the human brain uses something on the order of 10-20W so 13.5W seems like there's probably some room to improve on a 4b model!
Re:DeepX claims inference up to 100B parameters at less than... by delichon on Monday November 09, 2026 @03:29PM (#50431573)
423. Could y'all please start prefixing comments with the number of joules you consumed in generating it, so that I can decide if it's worth reading?
Re:DeepX claims inference up to 100B parameters at less than... by Terr_ on Monday November 09, 2026 @03:36PM (#50431695)
[delayed]
Re:DeepX claims inference up to 100B parameters at less than... by snapplebobapple on Monday November 09, 2026 @05:23PM (#50433138)
Eleventy billion:watch snl celebrity jeopardy.

^what every advertiser will do if we actually do this lol

Re:DeepX claims inference up to 100B parameters at less than... by dinfinity on Monday November 09, 2026 @04:25PM (#50432412)
Statements like these often overlook that a human brain is pretty slow for a lot of tasks. It's hard to make direct comparisons, but for language (related cognitive work) we're at maybe a few tokens per second.

I wonder how even a team of humans (a meat mixture of experts) would perform on the AI benchmarks, measured in energy use per intelligence index/benchmark point.

Re:DeepX claims inference up to 100B parameters at less than... by egberts1 on Monday November 09, 2026 @04:34PM (#50432536)
[delayed]
Re:DeepX claims inference up to 100B parameters at less than... by danuker on Monday November 09, 2026 @05:22PM (#50433128)
Back in July, I timed myself manually looking at some data files to create an index out of them, and out of curiosity asked some AI to do the same.

It took me an hour (61 minutes) and I made 4 mistakes in about 80 entries. It took DeepSeek-V4-Flash 3 minutes, it cost $0.034, and only made 2 mistakes. There is no contest. Humans have lost.

Re:DeepX claims inference up to 100B parameters at less than... by anonymous908213 on Monday November 09, 2026 @05:39PM (#50433292)
Uh, yeah, humans lost at data processing about halfway through the last century. The calculator can calculate fast than you. Great that you've finally discovered what computers can do, but it doesn't mean what you seem to think it means.
Re:DeepX claims inference up to 100B parameters at less than... by latentsea on Monday November 09, 2026 @06:41PM (#50433983)
These days I like to think of myself as a 100T dense model with 20 t/s prefill and 2 t/s decode. I am an RL(HF/VR/CR) policy rollout at this point.
Re:DeepX claims inference up to 100B parameters at less than... by K0balt on Tuesday November 10, 2026 @01:58AM (#50436968)
I know some people that would beg to differ. They’re at least 7B class.
Re:DeepX claims inference up to 100B parameters at less than... by NewJazz on Monday November 09, 2026 @03:19PM (#50431384)
[delayed]
Re:DeepX claims inference up to 100B parameters at less than... by gunalx on Monday November 09, 2026 @04:08PM (#50432161)
Not really. Models of that size is reasonably often moe. It is also 100b even if it only has 20b active at a time.
Re:DeepX claims inference up to 100B parameters at less than... by sampton on Monday November 09, 2026 @04:03PM (#50432100)
13.5W is Jetson nano territory.