Aug 2026· Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication· 0 citations· 18 references
Computer Science
TL;DR
DynamoServe is presented, a multi-tenant LLM serving framework that addresses challenges through three key innovations: leveraging stranded GPU memory to offload model weights and KV caches, mitigating resource fragmentation in multi-workload environments, and improving memory locality through coordinated data placement and demand-driven weight migration across GPUs.
Abstract
The rapid adoption of large language models (LLMs) has increased the need for efficient multi-tenant inference systems that maximize GPU utilization. However, existing frameworks struggle to scale due to the high memory demands of model weights and key-value (KV) caches. We present DynamoServe, a multi-tenant LLM serving framework that addresses these challenges through three key innovations: (1) leveraging stranded GPU memory to offload model weights and KV caches, (2) mitigating resource fragmentation in multi-workload environments, and (3) improving memory locality through coordinated data placement and demand-driven weight migration across GPUs. Together, these techniques enable high-throughput, low-latency inference. Experiments on state-of-the-art models show that DynamoServe significantly improves memory efficiency without sacrificing latency.
Existing Large Language Model (LLM) inference systems often rely on static model placement and scheduling policies, which struggle to handle heterogeneous and dynamic real-world workloads. The key challenge is to adapt serving strategies to workload fluctuations while keeping reconfiguration overhead minimal. In this paper, we present OrionInfer, an adaptive LLM serving system that aligns inference strategies with real-time demand. OrionInfer introduces three key techniques: (1) runtime switching between data parallelism and tensor parallelism with negligible overhead; (2) an efficient inference pipeline that preserves batching efficiency during parallelism transitions; and (3) live-migration-based load balancing to alleviate memory pressure and improve resource utilization. Evaluations across multiple model scales show that OrionInfer delivers robust performance under diverse serving scenarios. In end-to-end serving, it reduces average TTFT by up to 25% over DP-priority configurations under low loads and lowers P99 tail latency by 50%--90% over TP-priority configurations under most high-traffic settings. In disaggregated prefill serving, OrionInfer improves prefill completion time (PCT) SLO attainment by up to 16.5 percentage points over DP-priority static baselines and reduces P99 PCT by up to 74.7% over TP-priority static baselines. Compared with dynamic baseline, OrionInfer provides better tail-latency stability, reducing P99 PCT by 38.6%--40.8% while avoiding the extra memory footprint.
Jingqi Feng, Guang Yang, Yukai Huang et al.· Proceedings of the 32nd ACM...· 0 citations
Long-context, multi-turn, and agentic LLM workloads increasingly reuse previously processed context, making KV-cache reuse essential for reducing redundant computation. However, this reuse shifts the bottleneck to the memory tier that stores and serves reusable KV states at cluster scale. GPU HBM and host DRAM are too costly to scale to TB-scale shared context capacity, motivating remote tiers built from lower-cost, higher-capacity media. This paper presents HyMCache, a CXL memory rack for multi-turn LLM serving. We build the memory rack using cost-efficient CXL-hybrid memory (CXL-HM), which combines a small amount of in-device DRAM with large SSD-backed capacity behind a CXL interface. By exploiting the read-dominant, predictable, and append-only nature of multi-turn KV-cache access, HyMCache rethinks DRAM management within CXL-HM to efficiently support TB-scale SSD-backed KV reuse. It uses request-level prefix prefetching and opportunistic write buffering to stage latency-critical reads in device DRAM, enabling DRAM-scale KV-cache efficiency at SSD-level cost. We evaluate HyMCache on a real CXL-HM prototype under both single-aggregator and PD-disaggregated serving configurations. Under the same DRAM budget, HyMCache outperforms local LMCache by 3.0x in single-node serving and 1.45x in PD-disaggregated serving. Compared with 1 TB distributed-DRAM Mooncake, HyMCache incurs about 30% lower performance but uses 16x less DRAM.
Hakbeom Jang, Inho Song, Hoshik Kim et al.· 0 citations
This work presents Oneiros, a dynamic remapping engine for multi-tenant LLM serving that dynamically repurposes GPU memory allocated for model parameters as KV cache capacity, enabling nonblocking, unidirectional parameter transfer.
M-LoRA is proposed, a memory-aware multi-LoRA serving system that reduces queuing delay and improves throughput through efficient request scheduling guided by fine-grained memory modeling.
Shaolong Li, Xiang Yang, Qi Qi et al.· 0 citations
FLINT is proposed, a workload-driven HBF substrate for capacity-scalable LLM inference that integrates HBF as a memory-capacity tier alongside HBM while addressing three adoption challenges.
Geraldo F. Oliveira, Arash Tavakkol, Xiang-Yu Zhu et al.· 0 citations
Results show up to 2.3x lower remote-access overhead compared to TCP-based designs, a 13% increase in cluster-wide memory utilization at rack scale, and up to 63% reduction in miss rate for memory-constrained tenants under skewed workloads.
