The capabilities of artificial intelligence applications and modern processors, such as GPUs, are beginning to exceed data center architectures. While the intense power and cooling demands of generative AI are well reported, relatively little has been set about the memory bottleneck that currently pegs AI performance. But that is changing due to memory innovation.
AI needs memory. Lots of it. Yet traditional memory architectures can act as a bottleneck for AI performance. Thus, the introduction of Compute Express Link is timely. This new server interconnect opens the door to memory resources being pooled and shared. It can help to bridge the performance gap that sits between low-latency memory and solid-state drives. CXL can also lower the cost of memory, which is one of the most expensive elements of IT, while eliminating yet another barrier standing in the way of AI achieving its full potential.
Discover more about the subject in this TechRepublic Premium feature by Drew Robb.
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GEN AI EXPOSES BOTTLENECK
The presence of the latest generation of demanding workloads such as gen AI has brutally exposed a bottleneck in IT. As large language models get bigger, as processors and graphical processing units become more powerful, and as rack densities soar, traditional IT designs struggle to keep up.
But it is the memory bottleneck that has become most apparent. If you graph CPU cores against time and then factor in memory bandwidth, the divergence in trend lines is stark. Memory bandwidth expansion paralleled CPU core expansion for a while. But it has stalled for several chip generations while the core count grew ever higher. Result: CPUs are getting starved of memory to the point where you are not able to fully benefit from additional cores. As well as latency, once main memory is full, demanding applications can run into issues such as excessive memory copying, too much I/O being consumed by storage, excessive buffering, and out-of-memory errors. Any of these can crash applications if they go on too long.
Another common memory problem is underutilized or stranded memory resources. Some processors may be in dire need of memory, while others have an ample supply. This boils down to system design and limitations. DRAM is traditionally housed in dual in-line memory modules close to the CPU. With this design, other CPUs can only access certain DIMMs and not others.
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