EMA: Elastic and Performance Transparent Memory Across GPUs
Multi-GPU servers have become the standard building block of modern data centers, providing aggregated capacity through high-bandwidth interconnects. At the same time, workloads such as LLM inference exhibit highly dynamic memory demands, which can cause one GPU to exhaust its local memory while others remain underutilized. This mismatch motivates a model of elastic resource sharing across GPUs. We present EMA, a memory sharing system that allows GPUs within a server to borrow and reclaim memory from each other, forming an elastic pool of capacity. EMA ensures performance transparency for both borrowers and lenders. For borrowers, prefetching hides remote access costs so that applications...
| Authors | Yi Xu, Tian Xia, Ion Stoica |
|---|---|
| Primary category | cs.DC |
| Published | 22 September 2026 |
| Last updated at source | 22 September 2026 |
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