FlashOffloading: Low-Latency KV Cache Offloading for Long-Context LLM Serving
arXiv cs.AIen
arXiv:2611.07542v1 Announce Type: new Abstract: In large language model (LLM) serving systems, the key-value (KV) cache grows linearly with sequence length and concurrency, so long-context inference easily hits the high-bandwidth memory (HBM) capacity wall and results in low concurrency. Offloading the KV cache to host dynamic random-access memory (DRAM) frees HBM space and raises concurrency, but the additional cache management and host-to-device (H2D) load overhead inflates inference latency, higher concurrency fails to translate into effective throughput. We present FlashOffloading, a host-DRAM-centric inference architecture that fully offloads the KV cache in both the prefill and decode
This is a short summary published by AI Global Wire. The full article is owned and hosted by arXiv cs.AI — open it there to read it in full.
Read the full story at arXiv cs.AI- Forskning
- Företag
Related AI news
- Anthropic 遇 AI 股慘崩逆風、傳 IPO 估值恐少逾 3 成TechNews (TW) · November 6, 2026
- BlockLock: Reproducible LLM Inference with Fixed Batch GeometryarXiv cs.AI · November 6, 2026
- Align Before Aggregation: Basis-Consistent Federated LoRA under Heterogeneous RanksarXiv cs.AI · November 6, 2026
- KDASO: Knowledge-Data Dual-Driven Automated Skill Optimization for Liability Adjudication TasksarXiv cs.AI · November 6, 2026
- Adapting the Process of Adaptation: Darwinian, Lamarckian, and Agent-Directed Evolution in AIarXiv cs.AI · November 6, 2026
- SAVeR$^2$: Reasoning-based Safety Alignment for Large Reasoning Models via Verifiable RewardsarXiv cs.AI · November 6, 2026