CXL-SpecKV: A Disaggregated FPGA Speculative KV-Cache for Datacenter LLM Serving

• URL: http://arxiv.org/abs/2512.11920v1
• Date: Thu, 11 Dec 2025 15:40:36 GMT
• Title: CXL-SpecKV: A Disaggregated FPGA Speculative KV-Cache for Datacenter LLM Serving
• Authors: Dong Liu, Yanxuan Yu,
• Abstract summary: Large Language Models (LLMs) have revolutionized natural language processing tasks.<n>LLMs face challenges due to the massive memory requirements of key-value ( KV) caches.<n>We propose textbfCXL-SpecKV, a novel disaggregated KV-cache architecture.
• Score: 5.216774377033164
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have revolutionized natural language processing tasks, but their deployment in datacenter environments faces significant challenges due to the massive memory requirements of key-value (KV) caches. During the autoregressive decoding process, KV caches consume substantial GPU memory, limiting batch sizes and overall system throughput. To address these challenges, we propose \textbf{CXL-SpecKV}, a novel disaggregated KV-cache architecture that leverages Compute Express Link (CXL) interconnects and FPGA accelerators to enable efficient speculative execution and memory disaggregation. Our approach introduces three key innovations: (i) a CXL-based memory disaggregation framework that offloads KV-caches to remote FPGA memory with low latency, (ii) a speculative KV-cache prefetching mechanism that predicts and preloads future tokens' cache entries, and (iii) an FPGA-accelerated KV-cache compression and decompression engine that reduces memory bandwidth requirements by up to 4$\times$. When evaluated on state-of-the-art LLM models, CXL-SpecKV achieves up to 3.2$\times$ higher throughput compared to GPU-only baselines, while reducing memory costs by 2.8$\times$ and maintaining accuracy. Our system demonstrates that intelligent memory disaggregation combined with speculative execution can effectively address the memory wall challenge in large-scale LLM serving. Our code implementation has been open-sourced at https://github.com/FastLM/CXL-SpecKV.

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