GPU Cache Placement: Insights for Efficient Sessions
A new study explores how to best allocate key-value (KV) caches across different memory tiers—Graphics Processing Unit (GPU), CPU, and Solid State Drive (SSD)—to optimize session management in AI systems. The research uses a discrete event simulator to analyze the performance of various placement strategies for chat, agent loops, and document question answering workloads. Key findings include…
1 source primary source
Key points
- Tiering GPU HBM with CPU DRAM and SSD supports up to 73 times more concurrent sessions per GPU
- Predicted reuse policy performs similarly to recency strategy on some workloads
- Prefetching does not justify its bandwidth cost
Read the original at arXiv cs.AI · by Srikanta Datta Tumkur, Jay Iyer, Mehar Simhadri, Sai Pavan Kumar, Sai Kapil Kumar, Ramesh Nampelly primary sourceOpen source ↗
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