Memory Matters: CXL 2.0 Strategies
As AI takes center stage in the enterprise, system memory has taken central stage, as systems rush to feed compute with low latency to drive the AI pipeline and conduct everything from analytics to a mix of highly parallel workloads. The question becomes, how do you scale memory without breaking the IT budget? Meet CXL 2.0, a supercharger for enhanced memory activation that places more capacity into IT reach and does so by accessing stranded memory.
How is this possible, and what is stranded memory? In many systems today, even as compute reaches peak utilization, memory sits underused. I think of this as a stranded resource where something of high value sits underutilized. At the same time, another host might be starved for memory capacity. This fragmentation can be wasteful, especially when memory constrained applications like AI workloads and vector databases require capacity.
Enter CXL 2.0. This innovative standard brings memory pooling, tiering, and switching capabilities that let memory across systems behave as a shared, dynamically allocated resource pool, rather than rigid per-socket islands. Early implementations have shown that memory pooling across 8–16 sockets can reduce DRAM cost while keeping performance at near parity with traditional architectural approaches.
Want to take advantage of this game-changing technology? Here are three deployment strategies I recommend:
Memory pooling across adjacent nodes
Deploy CXL switches with CXL 2.0 enabled systems to aggregate memory, allowing hosts to draw from a common memory pool depending on application requirements. This is ideal for workloads with variable memory footprints. You may see slightly higher latency compared to local DRAM, so deploy pool sizes where your latency tradeoff is acceptable.
Tiered memory architectures
In this model, the system maps “hot” data to local DRAM, and overflow or less-critical data to CXL-attached memory. The OS or memory manager dynamically moves pages. This approach offers better aggregate capacity without overprovisioning.
Be mindful that software within these configurations must be intelligent about page placement.
Dynamic rebalancing / elastic memory allocation
This model allows for dynamic migration and reallocation based on workload priority in real time. This flexibility helps especially in multi-tenant or bursty environments.
To try out these new approaches in your live environments, it’s important to do your homework, carefully measuring application capability for various approaches and implementing solutions that best meet your environment’s demands. Pooling, tiering, or dynamic rebalancing each have tradeoffs, so pick the model best aligned to your workload requirements. Regardless of your path ahead, Xeon 6 processors with integrated CXL 2.0 support is a future-ready foundation for advanced memory applications.
As AI takes center stage in the enterprise, system memory has taken central stage, as systems rush to feed compute with low latency to drive the AI pipeline and conduct everything from analytics to a mix of highly parallel workloads. The question becomes, how do you scale memory without breaking the IT budget? Meet CXL 2.0, a supercharger for enhanced memory activation that places more capacity into IT reach and does so by accessing stranded memory.
How is this possible, and what is stranded memory? In many systems today, even as compute reaches peak utilization, memory sits underused. I think of this as a stranded resource where something of high value sits underutilized. At the same time, another host might be starved for memory capacity. This fragmentation can be wasteful, especially when memory constrained applications like AI workloads and vector databases require capacity.
Enter CXL 2.0. This innovative standard brings memory pooling, tiering, and switching capabilities that let memory across systems behave as a shared, dynamically allocated resource pool, rather than rigid per-socket islands. Early implementations have shown that memory pooling across 8–16 sockets can reduce DRAM cost while keeping performance at near parity with traditional architectural approaches.
Want to take advantage of this game-changing technology? Here are three deployment strategies I recommend:
Memory pooling across adjacent nodes
Deploy CXL switches with CXL 2.0 enabled systems to aggregate memory, allowing hosts to draw from a common memory pool depending on application requirements. This is ideal for workloads with variable memory footprints. You may see slightly higher latency compared to local DRAM, so deploy pool sizes where your latency tradeoff is acceptable.
Tiered memory architectures
In this model, the system maps “hot” data to local DRAM, and overflow or less-critical data to CXL-attached memory. The OS or memory manager dynamically moves pages. This approach offers better aggregate capacity without overprovisioning.
Be mindful that software within these configurations must be intelligent about page placement.
Dynamic rebalancing / elastic memory allocation
This model allows for dynamic migration and reallocation based on workload priority in real time. This flexibility helps especially in multi-tenant or bursty environments.
To try out these new approaches in your live environments, it’s important to do your homework, carefully measuring application capability for various approaches and implementing solutions that best meet your environment’s demands. Pooling, tiering, or dynamic rebalancing each have tradeoffs, so pick the model best aligned to your workload requirements. Regardless of your path ahead, Xeon 6 processors with integrated CXL 2.0 support is a future-ready foundation for advanced memory applications.
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