Communities
Connect sessions
AI calendar
Organizations
Join Slack
Contact Sales

Terms and Conditions

Twitter GitHub LinkedIn Bluesky Youtube

© 2026 ResearchTrend.AI, All rights reserved.

Home
Papers
2202.01306
Cited By

Harmony: Overcoming the Hurdles of GPU Memory Capacity to Train Massive
DNN Models on Commodity Servers

v1v2 (latest)

Harmony: Overcoming the Hurdles of GPU Memory Capacity to Train Massive DNN Models on Commodity Servers

Proceedings of the VLDB Endowment (PVLDB), 2022

2 February 2022

Amar Phanishayee

Jakub Tarnawski

ArXiv (abs)PDF HTML

Papers citing "Harmony: Overcoming the Hurdles of GPU Memory Capacity to Train Massive DNN Models on Commodity Servers"

2 / 2 papers shown

ProTrain: Efficient LLM Training via Memory-Aware Techniques

ProTrain: Efficient LLM Training via Memory-Aware Techniques

228

3

0

12 Jun 2024

FlexGen: High-Throughput Generative Inference of Large Language Models
with a Single GPU

FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPUInternational Conference on Machine Learning (ICML), 2023

...

Joseph E. Gonzalez

Abigail Z. Jacobs

Christopher Ré

450

569

0

13 Mar 2023