GPU Management: Why Idle GPUs Are the New Grounded Aircraft

How Much Progress Has There Been in NVIDIA Datacenter GPUs?

arXiv:2601. 20115v3 Announce Type: replace-cross Abstract: As the role of modern Graphics Processing Units (GPUs) becomes increasingly essential for several computing tasks, analyzing their past and current progress is paramount for determining future constraints on scientific research.

How Much of a Data Science Workflow Can Run on a GPU Today? Part 1: Accelerating Data Preparation

Exploring GPU acceleration with cuDF, cudf. pandas, and the Polars GPU Engine The post How Much of a Data Science Workflow Can Run on a GPU Today?

When GPU Utilization Lies: The Hidden Systems Problem Slowing Modern AI

Why “average utilization” lies about how full your GPUs really are The post When GPU Utilization Lies: The Hidden Systems Problem Slowing Modern AI appeared first on Towards Data Science .

WattGPU: Predicting Inference Power and Latency on Unseen GPUs and LLMs

Large Language Model (LLM) inference workloads are a rapidly growing contributor to data center energy consumption. Optimizing these deployments requires matching specific LLMs to the most efficient GPUs, but operators currently lack the tools to do so without exhaustively profiling each combination.

WattGPU: Predicting Inference Power and Latency on Unseen GPUs and LLMs

arXiv:2607. 02391v1 Announce Type: cross Abstract: Large Language Model (LLM) inference workloads are a rapidly growing contributor to data center energy consumption.

DumpsterCluster: From Dumpster Diving to Serving LLaMA-70B on $60 GPUs

arXiv:2608. 14614v1 Announce Type: cross Abstract: As AI datacenters retire functional GPUs, vast quantities of still capable accelerators enter secondary markets.

From Zero to GPU: A Guide to Building and Scaling Production-Ready CUDA Kernels

DynaResize: Runtime GPU Reallocation for Disaggregated LLM Post-Training

arXiv:2607. 22614v1 Announce Type: new Abstract: RL-based LLM post-training increasingly disaggregates Rollout and Training across separate GPU resources, but static GPU partitioning suffers from severe pipeline bubbles under long-tail rollout latency.

Benchmarking Confidential GPU Inference on NVIDIA H100 under Intel TDX

arXiv:2607. 19353v1 Announce Type: new Abstract: Confidential computing is becoming a practical deployment requirement for AI inference workloads that process sensitive inputs or protect proprietary model assets.

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.

Behind the Scenes of Distributed Training and Why Your GPU Wiring Matters as Much as Your Strategy

A measured look at distributed training, from DDP and FSDP to the ZeRO stages in between, and why the wiring between your GPUs matters as much as the strategy you choose The post Behind the Scenes of Distributed Training and Why Your GPU Wiring Matters as Much as Your Strategy appeared first on Towards Data Science .

A principled approach for energy-efficient training via phase-aware GPU frequency tuning

arXiv:2609.24205v1 Announce Type: cross Abstract: Modern AI model training imposes unprecedented computational demands, making it a key contributor to datacenter energy consumption. Yet a significant...