Bridging LLM Serving and CXL-SSDs with Chunk-Aware KV Cache Management
cs.AR, cs.AI, cs.ET
Submitted: 2026-09-20
Updated: 2026-09-20
Comments: 12 pages, 17 figures
Code: https://github.com/LMCache/LMBenchmark
License: http://creativecommons.org/licenses/by/4.0/
The gist: NAND-backed storage offers the capacity needed to scale LLM prefix caching, but its block I/O path incurs CPU cache contention and host-DRAM staging in addition to NAND latency.
Terminology
Abstract
NAND-backed storage offers the capacity needed to scale LLM prefix caching, but its block I/O path incurs CPU cache contention and host-DRAM staging in addition to NAND latency. Our characterization shows that these interface costs persist even with DRAM as the storage medium, motivating CXL-SSDs for byte-addressable access to NAND-backed capacity. Surprisingly, however, a stock CXL-SSD remains about 3 times slower than local DRAM and no faster than an NVMe SSD, while generic prefetching provides little benefit. We present LM-CXD, a CXL-SSD specialized for LLM prefix caching. LM-CXD bridges the semantic gap between the serving engine, which knows which KV chunks will be consumed, and the device, which controls their placement and movement. It makes KV chunks device-visible I/O units, exposes NAND-to-DRAM progress to the serving engine, and uses device DRAM as a GPU-accessible buffer. LM-CXD further coordinates request scheduling with windowed prefetching and pipelines layerwise KV movement with GPU computation to hide NAND latency under limited device DRAM. Across five LLM models, LM-CXD reduces average TTFT over a stock CXL-SSD by up to 2.6 times with compute asynchronous prefetching and 4.03 times with layerwise prefetching, achieving TTFT within 1.5 times of local DRAM on average.
Sources
- ITME: Inference Tiered Memory Expansion with Disaggregated CXL-Hybrid Memories
- GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints
- A CXL Memory Rack for Multi-Turn LLM Serving
- A Comprehensive Survey on Long Context Language Modeling
- Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving
- LMCache: An Efficient KV Cache Layer for Enterprise-Scale LLM Inference
- Performance Characterizations and Usage Guidelines of Samsung CXL Memory Module Hybrid Prototype
- TraCT: Disaggregated LLM Serving with CXL Shared Memory KV Cache at Rack-Scale
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