ExpertPlex: A High-Goodput Disaggregated Serving System for MoE LLMs with Adaptive Persistent Kernels

MoE Expert Execution in Disaggregated LLM Serving with a High-Bandwidth ReRAM Near-Memory Architecture

A ReRAM near-memory architecture that keeps expert weights resident behind high-bandwidth local reads and recovers occupancy with bounded core-local multicast pooling, coactivation-aware placement, and load-aware fetch, and sizes each communication level from induced demand is presented.

CELLServe: An SLO-Aware and Cost Efficient LLMs Serving System for Serverless Computing Environments

CELLServe formalizes SLO-constrained joint resource provisioning as an optimization problem with a dedicated algorithm, and introduces an opportunistic instance merging strategy for decode phase functions to reclaim fragmented resources.

Aurora: A Disaggregated GPU-PNM-PIM System for High-Throughput Mixed-Length LLM Inference

Modern large language model (LLM) inference is increasingly dominated by memory-bound operations, making processing-near-memory with processing-in-memory (PNM-PIM) an attractive approach for accelerating the decode phase. However, recent long-context LLMs adopt interleaved local and global attention (ILGA), which introduces heterogeneous execution characteristics across transformer blocks. In realistic serving environments, this heterogeneity is further amplified by mixed-length requests, where inputs with widely different context lengths are processed concurrently. These trends break a key assumption underlying existing PNM-PIM systems that transformer blocks exhibit similar latency and resource demands and can be efficiently mapped to a uniform pipeline. Under ILGA and mixed-length workloads, this assumption no longer holds, leading to severe pipeline imbalance and low utilization in prior PNM-PIM designs. Moreover, PNM-PIM–only systems struggle to efficiently support long-context prefill, which remains compute-intensive and is better suited for GPU execution. In this paper, we propose Aurora, a GPU–PNM–PIM disaggregated system designed to efficiently serve mixed-length LLM inference under ILGA. Aurora introduces an ILGA-aware multi-path PNM-PIM pipeline that explicitly accounts for block-level heterogeneity and request-length diversity, improving pipeline utilization without overprovisioning tensor parallelism. Also, Aurora further adopts a stream-oriented Softmax design to reduce stage-level latency imbalance during decoding. To enable end-to-end inference under disaggregation, Aurora treats KV cache transfer as a pipeline operation and coordinates request issuance and scheduling to avoid transfer-induced stalls. Our experimental results show that Aurora achieves up to 8.5 × and 2.2 × higher throughput than the GPU-only baseline and prior PNM-PIM systems, respectively, in end-to-end Llama4-Scout inference, while requiring a comparable or smaller number of devices.

Beyond Prefill-Decode Disaggregation: Dissecting LLM Inference for Heterogeneous Platforms via Dynamic Operator Scheduling

Prefill-decode disaggregation (PD) and roofline-based operator placement are common strategies for partitioning Large Language Model (LLM) inference across heterogeneous systems, but they are often insufficient in practice. End-to-end latency also depends on workload shape, runtime device contention, and persistent weight layout. We present DOPS (dynamic operator scheduling), a hardware-aware, closed-loop framework that jointly optimizes operator scheduling and blockwise weight layouts. DOPS constructs a stage-aware directed acyclic graph (DAG) and integrates two components: the Bifocal scheduler for dynamic operator-to-device placement and the Weight Layout Arbiter (WLA) for selecting hardware-efficient weight layouts under strict memory constraints. Across representative heterogeneous systems combining neural processing units (NPUs) and processing-in-memory (PIM) devices, Bifocal achieves geometric-mean speedups of 1.20$\times$ to 2.23$\times$ over the PD baseline. WLA provides an additional geometric-mean speedup of 1.28$\times$ to 1.33$\times$ over Bifocal/Linear. DOPS also supports systematic analysis of workload sensitivity and hardware scalability for LLM serving. The source code is available at https://github.com/YIAI-02/TriForm, and the visualization tool is demonstrated at https://youtu.be/Ya_oMCyYno0.

SplitScaling: Adaptive Scaling for Disaggregated LLM Serving Against Traffic Bursts via DRL

BlockServe: Block-Grained Continuous Batching for High-Throughput Diffusion LLM Serving

Efficient serving of diffusion large language models (dLLMs) is hindered by convergence heterogeneity: when batching multiple requests, different sequences converge at different rates, causing faster requests to stall behind slower stragglers and introducing compute bubbles and tail latency. We present BlockServe, a continuous batching framework that integrates block-grained scheduling -- immediately evicting completed requests at block boundaries -- with mixed-state execution that extends dual cache and parallel decoding to heterogeneous batches via gather-scatter indexing. Furthermore, a compute-aware admission controller expands effective batch capacity through token-budgeted refill. On Dream and LLaDA across five benchmarks, BlockServe achieves 1.9--10.6$\times$ throughput over Fast-dLLM with comparable generation quality, establishing block-grained scheduling as a foundation for high-throughput offline dLLM inference.