ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2211.04515
  4. Cited By
QuantPipe: Applying Adaptive Post-Training Quantization for Distributed
  Transformer Pipelines in Dynamic Edge Environments

QuantPipe: Applying Adaptive Post-Training Quantization for Distributed Transformer Pipelines in Dynamic Edge Environments

8 November 2022
Hong Wang
Connor Imes
Souvik Kundu
P. Beerel
S. Crago
J. Walters
    MQ
ArXivPDFHTML

Papers citing "QuantPipe: Applying Adaptive Post-Training Quantization for Distributed Transformer Pipelines in Dynamic Edge Environments"

2 / 2 papers shown
Title
FedPaI: Achieving Extreme Sparsity in Federated Learning via Pruning at Initialization
FedPaI: Achieving Extreme Sparsity in Federated Learning via Pruning at Initialization
Haonan Wang
Ziqiang Liu
Kajimusugura Hoshino
Tuo Zhang
J. Walters
S. Crago
49
0
0
01 Apr 2025
Megatron-LM: Training Multi-Billion Parameter Language Models Using
  Model Parallelism
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
M. Shoeybi
M. Patwary
Raul Puri
P. LeGresley
Jared Casper
Bryan Catanzaro
MoE
245
1,826
0
17 Sep 2019
1