And this problem only gets worse as you scale up. ![]() You could drop down to 10 channels and get closer to your target, but then you're going to take a hit to memory bandwidth and pay extra for the privilege. However, 32GB DIMMs would leave you 192GB short, while 64GB DIMMs would leave you with just as much in surplus. Using a 96-core AMD Epyc 4-based system with one DIMM per channel, you'd need at least 576GB of memory. Say your workload benefits from having 3GB/thread. Take this thought experiment as an example: Why Intel killed its Optane memory business.Why you should start paying attention to CXL now.Astera Labs says its CXL tech can stick DDR5 into PCIe slots.How AMD, Intel, Nvidia are keeping their cores from starving."Going from 32GB to 48GB to 64GB and 96GB offers gentler price increments." The cost per bit is fairly constant, therefore, if you keep doubling, the cost increments becomes prohibitively expensive," Lam explained. "Doubling of DRAM capacity - 32GB to 64GB to 128GB - now produces large steps in cost. And in the cloud, some industry pundits put that number closer to 50 percent. Take 20 of these chips and bake them onto a DIMM, and you're left with 48GB of usable memory after you take into account ECC and metadata storage.Īs our sister site The Next Platform reported earlier this year, memory can account for as much as 14 percent of a server’s cost. Instead of the 16Gb - that's gigabit - modules found on most DDR5 memory today, non-binary DIMMs use 24Gb DRAM chips. What makes non-binary memory different from standard DDR5 comes down to the chips used to make the DIMMs. Non-binary memory isn't actually all that special. The added flexibility offered by these DIMMs could end up driving down system costs, as customers are no longer forced to buy more memory than they need just to keep their workloads happy. You can now have DIMMs with 24GB, 48GB, 96GB, or more in capacity. Instead of jumping straight from a 32GB DIMM to a 64GB one, DDR5, for the first time, allows for half steps in memory density. But with the introduction of DDR5 and non-binary memory in the datacenter, all of that's changing. As capacity goes up, it follows a predictable binary scale doubling from 8GB to 16GB to 32GB and so on. The January Effect seems to affect small caps more than mid-caps or large caps because they are less liquid.We're all used to dealing with system memory in neat factors of eight. The January Effect is a hypothesis, and like all calendar-related effects, it suggests that the markets as a whole are inefficient, as efficient markets would naturally make this effect non-existent. More to the point, over the past 30 years, January gains have occurred 17 times (57%), while losing January months numbered 13 (43%), barely better than the flip of a coin.Like other market anomalies and calendar effects, the January Effect is considered by some to be evidence against the efficient markets hypothesis.The January Effect is theorized to occur when investors sell losers in December for tax-loss harvesting, only to re-buy new positions in January. ![]()
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