Predictive Multi-Tier Memory Management for KV Cache in Large-Scale GPU Inference

Researchers present a unified system for optimizing KV cache memory management in large-scale GPU inference, addressing three critical inefficiencies through architecture-aware sizing, multi-tier memory hierarchy spanning CPU to NVMe storage, and predictive eviction policies. The approach achieves 70-84% cache hit rates and projects 1.4-2.1x improvements in latency and 1.7-2.9x throughput gains while reducing costs by 47% compared to existing solutions.

This technical advancement addresses a fundamental bottleneck in deploying large language models at scale. As GPU inference becomes increasingly central to AI service economics, memory bandwidth and capacity constraints directly impact both operational costs and user experience. The current state of KV cache management forces significant inefficiencies: systems over-provision memory by up to 57x, restrict caching to expensive GPU HBM rather than leveraging cheaper hierarchical storage, and reactively discard potentially reusable computation state.

The research emerges amid growing pressure to reduce AI inference costs as competition intensifies among model serving platforms. Enterprise deployments face mounting infrastructure bills as model sizes and inference volumes scale. Multi-tier memory hierarchies—combining GPU HBM, CPU DRAM, CXL-attached memory, and NVMe—have become physically available in modern data centers but remain underutilized by inference frameworks.

The system's three-pronged approach tackles distinct pain points: unified sizing prevents wasteful over-allocation, predictive caching with Bayesian statistics replaces reactive eviction, and architecture-aware optimizations address previously unsupported attention variants like multi-head latent attention. Achieving 70-84% cache hit rates with sub-millisecond latency for frequently accessed entries suggests the solution can maintain responsiveness while dramatically expanding effective capacity from 40GB to 38TB per node.

For infrastructure operators and model serving platforms, these improvements translate directly to reduced capital expenditure and operational overhead. Organizations currently constrained by GPU memory availability could increase batch sizes 7.4x, improving hardware utilization. Market adoption depends on integration into production serving frameworks and validation across diverse workload patterns beyond the tested scenarios.

• →Architecture-variant-aware sizing eliminates up to 57x memory over-provisioning through exact requirement calculations per attention type
• →Six-tier memory hierarchy extends KV cache capacity from 40GB to 38TB per node while maintaining sub-millisecond latency
• →Bayesian predictor achieves 70-84% cache hit rates, reducing redundant recomputation compared to reactive eviction policies
• →Projected 1.4-2.1x latency reduction and 1.7-2.9x throughput improvement with 47% cost savings versus current baselines
• →7.4x higher batch sizes possible through optimized memory allocation, improving GPU utilization and inference economics