  Upcoming CXL-based disaggregated memory devices feature special purpose units
to offload compute to near-memory. In this paper, we explore opportunities for
offloading compute to general purpose cores on CXL memory devices, thereby
enabling a greater utility and diversity of offload.
  We study two classes of popular memory intensive applications: ML inference
and vector database as candidates for computational offload. The study uses Arm
AArch64-based dual-socket NUMA systems to emulate CXL type-2 devices.
  Our study shows promising results. With our ML inference model partitioning
strategy for compute offload, we can place up to 90% data in remote memory with
just 20% performance trade-off. Offloading Hierarchical Navigable Small World
(HNSW) kernels in vector databases can provide upto 6.87$\times$ performance
improvement with under 10% offload overhead.
