if you want an independent cloud services provider for home servers vpns or clients consider the node and sign up to dead lenore dot com tech tech potato for a free NUMBER_TOKEN NUMBER_TOKEN day credit a recent garden performance report shows the node's topology offers almost double the database performance per dollar than other public cloud services so ever since a company called cerebros promoted our hot chips i've been kind of following them speaking to the ceo fair amount whenever they have a new customer new product launch current generation product is the wife scale engine NUMBER_TOKEN it is a wafer-sized processor literally yield of NUMBER_TOKEN built on tsmc seven nanometer NUMBER_TOKEN NUMBER_TOKEN cores it's got about NUMBER_TOKEN NUMBER_TOKEN gigabytes onboard sram the idea is that you do machine learning on this and because it's so big you can get models that require hundreds of gpus to fit on one of these and you get the inherent latencies of not having to manage data moving around those gpus so the downside is that they cost a lot so you've heard that tsmc seven nanometer maybe costs NUMBER_TOKEN grand wafer um i know i did a video earlier that was a lot lower than that though prices since then have gone up even if you include the custom packaging the custom ip that they've got in that cerebros processor because to power a whole wafer you need thousands and thousands of amps i believe the whole chip is about NUMBER_TOKEN kilowatts and the system it goes in is uh what NUMBER_TOKEN kilowatts of power supplies and some of those are redundant uh but the whole unit itself costs uh a few million exactly what they won't tell us it depends on the customer depends on the deployment but along that you side you know you get the software with it and the whole idea is that you replace racks and racks and racks of gpus with just this one system and you can chain up to NUMBER_TOKEN of these systems using their new swarmx and memory x interconnect it's all designed to be you know customized and scalable due up to machine learning issues without NUMBER_TOKEN trillion parameters which is you know an order of magnitude currently what's available and do it in the uh the idea is do it in a more cost-effective way assuming you already own the hardware and cerebros's current customers are very much typical uh you know department of defense lawrence livermore um national labs sandia i think has got one as well uh pharmaceutical industries got one uh astrazeneca oil and gas sort of those traditional companies that know how to do hpc at scale and are looking for a way to attach a big ai engine onto their supercomputer or use it separately there's also a few other use cases with a chip this big and with the amount of memory each of those NUMBER_TOKEN NUMBER_TOKEN cores have you can actually do stencil compute and this is harks back to a bit of what i did during my phd stencil compute is can be difficult to do or no stencil compute at a certain degree is difficult to paralyze but when you have enough memory per one of these cores you can do a full side-blown sensor compute and accelerate that paper is really fascinating if you're into that sort of thing i highly recommend you read it but the news i'm
START_SPONSOR_TOKEN if you want an independent cloud services provider for home servers vpns or clients consider the node and sign up to dead lenore dot com tech tech potato for a free NUMBER_TOKEN NUMBER_TOKEN day credit a recent garden performance report shows the node's topology offers almost double the database performance per dollar than other public cloud services END_SPONSOR_TOKEN