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Taming Asynchronous CPU-GPU Coupling for Frequency-aware Latency Estimation on M

BACKGROUND
2 SIGNALSFIRST DETECTED 21 April 2026
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27 Jul
21 Apr
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27 Apr
Focus Session: Hardware and Software Techniques for Accelerating Multimodal Foundation Models

arXiv:2604.21952v1 Announce Type: cross Abstract: This work presents a multi-layered methodology for efficiently accelerating multimodal foundation models (MFMs). It combines

REPORT · WHAT · ArXiv CS.AIOPEN STORY
12 Aug
LLMVisor: A Real-Time Latency Attribution Model for Multi-Tenant LLM Serving

arXiv:2608.08382v1 Announce Type: new Abstract: As LLM inference shifts to multi-tenant GPU clusters, co-batching improves throughput but obscures per-tenant usage and limits

REPORT · WHAT · ArXiv CS.AIOPEN STORY
13 May
GAR: Carbon-Aware Routing for LLM Inference via Constrained Optimization

arXiv:2605.11603v1 Announce Type: new Abstract: The growing deployment of large language models (LLMs) makes per-request routing essential for balancing response quality and

REPORT · HOW · ArXiv CS.AIOPEN STORY
22 May
PALS: Power-Aware LLM Serving for Mixture-of-Experts Models

arXiv:2605.21427v1 Announce Type: new Abstract: Large language model (LLM) inference has become a dominant workload in modern data centers, driving significant GPU utilization and

REPORT · WHAT · ArXiv CS.AIOPEN STORY
26 Mar
DAQ: Delta-Aware Quantization for Post-Training LLM Weight Compression

arXiv:2603.22324v1 Announce Type: cross Abstract: We introduce Delta-Aware Quantization (DAQ), a data-free post-training quantization framework that preserves the knowledge

REPORT · WHAT · ArXiv CS.AIOPEN STORY
30 Apr
AMMA: A Multi-Chiplet Memory-Centric Architecture for Low-Latency 1M Context Attention Serving

arXiv:2604.26103v1 Announce Type: cross Abstract: All current LLM serving systems place the GPU at the center, from production-level attention-FFN disaggregation to NVIDIA's Rubin

REPORT · WHAT · ArXiv CS.AIOPEN STORY
7 May
A Physics-Aware Framework for Short-Term GPU Power Forecasting of AI Data Centers

arXiv:2605.04074v1 Announce Type: cross Abstract: AI data centers experience rapid fluctuations in power demand due to the heterogeneity of computational tasks that they have to

REPORT · WHAT · ArXiv CS.AIOPEN STORY
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