Sponsored Post: We all know that inferencing and training AI models needs a lot of CPU muscle, but we don’t necessarily appreciate how important other components are in supporting AI and ML applications. …
Conventional wisdom says that trying to attach system memory to the PCI-Express bus is a bad idea if you care at all about latency. …
People have been talking about CXL memory expansion for so long that it seems that it should be here already, but with the dearth of CPUs that can support PCI-Express 5.0 peripherals we have to be patient a little bit longer. …
Dynamic allocation of resources inside of a system, within a cluster, and across clusters is a bin-packing nightmare for hyperscalers and cloud builders. …
Forget in-memory computing for the moment because it requires a complete re-architecting of applications and most of the time the underlying hardware, too. …
It is probably a myth that Bill Gates said “640 KB ought to be enough,” and whether or not he said it the truth is that it has never been enough. …
Energy efficiency and operating costs for systems are as important as raw performance in today’s datacenters. …
Increasing parallelism is the only way to get more work out of a system. …
Compute is by far still the largest part of the hardware budget at most IT organizations, and even with the advance of technology, which allows more compute, memory, storage, and I/O to be crammed into a server node, we still seem to always want more. …
We can talk about storage and networking as much as we want, and about how the gravity of data bends infrastructure to its needs, but the server – or a collection of them loosely or tightly coupled – is still the real center of the datacenter. …
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