KVDrive: A Holistic Multi-Tier KV Cache Management System for Long-Context LLM Inference

2026-05-18 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Expert, quick

Summary

KVDrive is a multi-tier key-value (KV) cache management system designed to address the significant memory demands of long-context Large Language Model (LLM) inference. Unlike existing offloading systems that store the full cache in host memory and fetch critical entries, KVDrive spans GPU memory, host DRAM, and SSD. It tackles the problem from a systems perspective, orchestrating cache placement, pipeline scheduling, and cross-tier coordination to sustain high-throughput inference under tight GPU budgets. KVDrive adapts cache management to attention behavior, restructures the decoding pipeline to overlap I/O and compute-bound stages, and harmonizes data movement across memory tiers. A functional prototype of KVDrive achieves up to 1.74x higher throughput compared to prior methods on long-context benchmarks with popular LLMs, while maintaining accuracy.

Key takeaway

For AI Engineers deploying long-context LLMs, KVDrive's multi-tier KV cache management offers a significant throughput improvement of up to 1.74x without accuracy degradation. You should investigate integrating similar holistic systems approaches to overcome memory bottlenecks and scale inference beyond current GPU and DRAM limits, especially when dealing with large batch sizes and extended context lengths.

Key insights

KVDrive optimizes long-context LLM inference by holistically managing KV cache across GPU, DRAM, and SSD tiers.

Principles

• Maximize cache reuse to minimize data movement.
• Overlap I/O and compute stages to eliminate stalls.
• Harmonize data movement across memory tiers.

Method

KVDrive jointly orchestrates cache placement, pipeline scheduling, and cross-tier coordination, adapting to attention behavior and restructuring the decoding pipeline to overlap I/O and compute.

In practice

• Utilize multi-tier memory for KV cache.
• Optimize data movement based on attention patterns.

Topics

• KVDrive
• KV Cache Management
• Long-Context LLM Inference
• Multi-Tier Memory Systems
• Decoding Pipeline Scheduling

Best for: MLOps Engineer, AI Engineer, CTO, AI Scientist, Machine Learning Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.