4 papers
Measuring the Inference Energy Efficiency of Open-Weight LLMs across Benchmarks, Reasoning Modes, and Hardware
Zeyu Yang, Wes Armour
Benchmark scores tell us what an LLM can do, but not how much energy is required to deliver that capability. We directly measure this trade-off for 86 open-weight model checkpoints…
Runtime-Orchestrated Second-Order Optimization for Scalable LLM Training
Yishun Lu, Junhao Zhang, Zeyu Yang +1
Second-order methods offer an attractive path toward more sample-efficient LLM training, but their practical use is often blocked by the systems cost of maintaining and updating la…
Second-Order Multi-Level Variance Correction for Modality Competition in Multimodal Models
Yishun Lu, Wes Armour
Autoregressive next-token training offers a unified formulation for image generation and text understanding, but it also creates strong modality competition that destabilizes optim…
CRIUgpu: Transparent Checkpointing of GPU-Accelerated Workloads
Radostin Stoyanov, Viktória Spišaková, Jesus Ramos +5
Deep learning training at scale is resource-intensive and time-consuming, often running across hundreds or thousands of GPUs for weeks or months. Efficient checkpointing is crucial…