AMD: Where we're going we don't need (as much) DRAM
So long as the pages are in DRAM before they're needed, the pages don't even need to be in the right order, he added.
Key points
- "The workhorse model is a transformer derivative model, just like ChatGPT or Claude or the other famous models, but instead of predicting, for example, in the case of text, what word is next, what we're doing is predicting what page of memory is going to be required next," Smerdon explained.
- From there, the model is further refined and retrained over time until it is able to predict accurately where the pages need to be and when they need to be there.
- This is made possible by using AI models to load memory pages in and out of cheaper NVMe storage before your operating system or apps ever know they need it — kind of like SWAP or a Windows page file, but smarter.
AMD acquired the tech from Mext back in June, and according to former CEO Gary Smerdon — now CVP of business development at AMD — the tech could cut enterprise server costs by as much as 83 percent by replacing costly RDIMMs with flash storage.
Additional details
- (Brace for it.) "We realized we could use AI to monitor the system and proactively push those pages back into DRAM, so that as far as the application or the operating system knew, they were there the whole time," Smerdon said.
- Everyone's favorite buzzword to the rescue Even in systems with high memory utilization, where (say) 95 percent of the memory is "in use," a lot of it isn't actively being used, he explained.
- Predicting which pages the system was going to need at which time isn't exactly trivial.
- 99 percent of the perf, half the DRAM According to AMD, the technology actually works.
- AMD's latest sales pitch: Up to 99 percent of the performance with half the DRAM, provided you're willing to buy a Mext license alongside your shiny new Epyc server.
How this summary was made: extractive summary — built from the sources' own reporting. It is drawn only from the reporting of the sources listed below — read the originals for full detail. How we summarize →
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