4 papers · 1 filter
A principled approach for energy-efficient training via phase-aware GPU frequency tuning
Miguel Braga, Júlio Pinto, Rahma Nouaji +5
Modern AI model training imposes unprecedented computational demands, making it a key contributor to datacenter energy consumption. Yet a significant fraction of the energy consume…
DeltaServe: Host-Agnostic Co-Serving of Inference and Fine-Tuning for LLMs
Jiaxuan Chen, Jianshu She, Ye Yuan +5
LLM serving systems are provisioned for peak load to meet strict latency targets, leaving substantial GPU compute idle whenever traffic falls below peak. We present DeltaServe, a h…
FOLD: Fuzzy Online Deduplication for Very Large Evolving Datasets via Approximate Nearest Neighbor Search
Nelson Bore, Pritish Mishra, Constantin Adam +2
Fuzzy deduplication is key to constructing large language model training corpora. However, classic Locality-Sensitive Hashing (LSH) pipelines scale poorly as corpora grow and are i…
MinatoLoader: Accelerating Machine Learning Training Through Efficient Data Preprocessing
Rahma Nouaji, Stella Bitchebe, Ricardo Macedo +1
Data loaders are used by Machine Learning (ML) frameworks like PyTorch and TensorFlow to apply transformations to data before feeding it into the accelerator. This operation is cal…