arXiv AI
Aug 3
Topology-Aware Data Movement for Disaggregated GPU Inference
arXiv:2607. 28633v1 Announce Type: cross Abstract: Disaggregated LLM inference creates a datacenter networking problem that no existing system solves correctly.
Gleam: Adaptive Network-Efficient CUDA API Remoting for Cross-Device GPU Sharing over LANs
arXiv:2607. 23115v1 Announce Type: cross Abstract: This paper aims to enable computation- and communication-efficient GPU sharing across devices within local area networks (LANs), facilitating ubiquitous AI inference on heterogeneous personal devices.
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arXiv AI
Jul 7
Data Driven Optimization of GPU efficiency for Distributed LLM-Adapter Serving
arXiv:2602. 24044v2 Announce Type: replace-cross Abstract: Large Language Model (LLM) adapters enable low-cost model specialization, but introduce complex caching and scheduling challenges in distributed serving systems where hundreds of adapters must be hosted concurrently.
arXiv AI
Jul 23
Fine-grained Computation-Communication Overlap via Tile-level Signaling and Scheduling for Mixture-of-Experts
arXiv:2607. 19539v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) architectures increase model capacity without proportionally increasing computation cost and have become a key building block for scaling large language models (LLMs) to trillion-parameter regimes.
arXiv AI
Sep 15
mKernel: Fast Multi-GPU, Multi-Node Fused Kernels
arXiv:2609.13585v1 Announce Type: cross Abstract: Communication has become a bottleneck in distributed training and inference of large models. Overlapping communication with computation at the granul...
arXiv AI
Jun 2
Lodestar: An Online-Learning LLM Inference Router
arXiv:2606. 00946v1 Announce Type: cross Abstract: Efficiently serving large language model (LLM) inference tasks is crucial both for user-perceived latency such as time-to-first-token (TTFT) and for GPU utilization.
arXiv AI
Sep 10
A Measurement Study of LLM Inference Trade-offs Across Edge Continuum Hardware
arXiv:2609.08307v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as backends for intelligent web services, but serving them across the edge continuum requires bala...
arXiv AI
Jul 14
Automated Tensor Scheduling for Hybrid CPU-GPU LLM Inference on Consumer Devices
arXiv:2607. 10183v1 Announce Type: cross Abstract: Running large language models on consumer devices such as laptops and desktops is challenging because model weights often exceed GPU memory capacity, making offloading inference necessary to extend effective model capacity with CPU memory.
arXiv AI
Jul 31
ServerlessT2I: Efficient Text-to-Image Workflow Serving on a Serverless Platform
arXiv:2607. 26566v1 Announce Type: cross Abstract: Text-to-image (T2I) workflows are increasingly deployed on serverless platforms because users often compose customized workflows and invoke them intermittently.
arXiv AI
Jun 9
Resource-aware Computation-Communication Overlap for multi-GPU ML Workloads
arXiv:2606. 09200v1 Announce Type: cross Abstract: The rapid growth of large-scale machine learning (ML) has made distributed training across multiple GPUs a fundamental component of modern ML systems.
arXiv Machine Learning
4d ago
Mira: Memory-Efficient MoE Inference Using Adaptive Caching and Predictive Expert Staging
arXiv:2609.38090v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models are a compelling architecture for scaling model capacity, making them especially attractive for deployment on resource-...
arXiv Machine Learning
Jun 2
ViBE: Co-Optimizing Workload Skew and Hardware Variability for MoE Serving
arXiv:2606. 00735v1 Announce Type: cross Abstract: In distributed Mixture-of-Experts (MoE) inference, input-dependent token routing interacts with GPU performance variability to create persistent stragglers under synchronized execution, where the slowest GPU determines layer latency.
arXiv AI
Aug 11
LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs
arXiv:2608. 07733v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) are widely used across domains such as natural sciences, social network analysis, chip design, and recommendation systems.