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Article <vmalpk$1pah$6@solani.org>
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From: Mild Shock <janburse@fastmail.fm>
Newsgroups: sci.math
Subject: Re: Memory Powering the AI Revolution
Date: Thu, 16 Jan 2025 11:07:51 +0100
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See also:

The Special Memory Powering the AI Revolution
https://www.youtube.com/watch?v=yAw63F1W_Us

Mild Shock schrieb:
> I currently believe that some of the fallacies
> around LLMs is that one assumes that the learning
> generates some small light NNs (Neural Networks),
> 
> which are then subject to blurred categories and
> approximative judgments. But I guess its quite
> different the learning generates very large massive NNs,
> 
> which can afford representing ontologies quite precise
> and with breadth. But how is it done? One puzzle piece
> could be new types of memory, so called High-Bandwidth
> 
> Memory (HBM), an architecture where DRAM dies are
> vertically stacked and connected using Through-Silicon
> Vias (TSVs). For example found in NVIDIA GPUs like the
> 
> A100, H100. Compare to DDR3 that might be found in
> your Laptop or PC. Could give you a license to trash
> L1/L2 Caches with your algorithms?
> 
>               HBM3                  DDR3
> Bandwidth    1.2 TB/s (per stack)  12.8 GB/s to 25.6 GB/s
> Latency      Low, optimized for    Higher latency
>               real-time tasks
> Power Efficiency
>               More efficient        Higher power consumption
>               despite high speeds   than HBM3