Predictive Multi-Tier Memory Management for KV Cache in Large-Scale GPU Inference
Key-value (KV) cache memory management is the primary bottleneck limiting throughput and cost-efficiency in large-scale GPU inference serving. Current systems suffer from three compounding inefficiencies: (1) the absence of unified KV cache sizing across all attention architectures--particularly multi-head latent attention (MLA), which is unsupported in general-purpose frameworks, resulting in up to 57x memory over-provisioning; (2) confinement of KV cache to a single memory tier (GPU HBM) despite the availability of a rich hierarchy spanning CPU DRAM, CXL-attached memory, NVMe via GPUDirect Storage, RDMA fabric, and parallel filesystems; and (3) reactive eviction policies that discard re...
| Authors | Sanjeev Rao Ganjihal |
|---|---|
| Primary category | cs.AR |
| Published | 19 April 2026 |
| Last updated at source | 7 August 2026 |
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