InstAttention: In-Storage Attention Offloading for Cost-Effective Long-Context LLM Inference

作者：Xiurui Pan, Erzhong Li, Qiao Li, Shengwen Liang, Yizhou Shan, Ke Zhou, Yingwei Luo, Xiaolin Wang, Jie Zhang · 年份：2025 · DOI：10.1109/hpca61900.2025.00113 · 被引用次数：21 · 研究领域：Advanced Neural Network Applications、Advanced Data Storage Technologies、Brain Tumor Detection and Classification

The widespread of Large Language Models (LLMs) marks a significant milestone in generative AI. Nevertheless, the increasing context length and batch size in offline LLM inference escalate the memory requirement of the key-value (KV) cache, which imposes a huge burden on the GPU VRAM, especially for resource-constrained scenarios (e.g., edge computing). Several cost-effective solutions leverage host memory or SSDs to reduce storage costs for offline inference scenarios and improve the throughput. Nevertheless, they suffer from significant performance penalties imposed by intensive KV cache accesses due to limited PCIe bandwidth. To address these issues, we propose InstAttention, a novel LLM inference system that offloads the most performance-critical computation (i.e., attention in decoding phase) and data (i.e., KV cache) parts to Computational Storage Drives (CSDs), which minimize the enormous KV transfer overheads. InstAttention designs a dedicated flashaware in-storage attention engine with KV cache management mechanisms to exploit the high internal bandwidths of CSDs instead of being limited by the PCIe bandwidth. The optimized P2P transmission between GPU and CSDs further reduces data migration overheads. Experimental results demonstrate that for a 13B model using an NVIDIA A6000 GPU, InstAttention improves throughput for long-sequence inference by up to $11.1 \times$, compared to existing SSD-based solutions such as FlexGen.