Next-generation memory and AI compute-in-memory

Conviction: 58% · Horizon: 3Y · 2026-06-18
AI workloads need faster memory architectures to unlock idle GPU compute

GPUs can perform massive parallel computation, but system performance is increasingly limited by the speed and efficiency of moving data from memory. Compute-in-memory and advanced memory materials could become valuable if they reduce this bottleneck for AI workloads.

Instrument Side Target Reason
SREMF Long A stake in a developer of next-generation memory and AI compute-in-memory provides leveraged exposure to a critical AI infrastructure bottleneck. If memory bandwidth and data movement remain limiting factors for GPU utilization, successful commercialization of this technology could create meaningful upside.

Themes

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