“We needed a partner who understood both the technical depth of Azure OpenAI and the governance requirements of an enterprise.”
Enterprise Program Brief
NVIDIAAdvanced
Data Parallelism: How to Train Deep Learning Models on Multiple GPUs
Explores how to train deep learning models across multiple GPUs using data-parallel training approaches.
Duration
8 hours
Level
Advanced
Format
Virtual, On-site, or Hybrid
Language
English
Executive brief for stakeholdersDownload Program Brief (PDF)
Enterprise Track
NVIDIA
Deep LearningAI
Data Parallelism: How to Train Deep Learning Models on Multiple GPUs
Distributed Deep Learning Frameworks
NVIDIA
On this page
Ideal for
ML EngineerDeep LearningTailored Team DeliveryImplementation-Focused
Audience Profile
Built for these roles
Built for teams moving into faster and larger-scale deep learning training.
Overview
Executive overview
Official NVIDIA DLI workshop on data parallelism for multi-GPU deep learning training.
Readiness
Prerequisites
- Solid deep learning and Python experience.
Program Outcomes
Capabilities your teams will gain
Understand multi-GPU training strategies
Apply data parallelism patterns to deep learning workloads
Curriculum
Curriculum roadmap
1
Multi-GPU training basics
2
Data-parallel scaling patterns
1Module 1
Scale training across GPUs
+
Module 1
Scale training across GPUs
Learn data-parallel approaches for training deep learning models more efficiently on multiple GPUs.
- Multi-GPU training basics
- Data-parallel scaling patterns
Delivery Models
Delivery models
Virtual ILTOnsiteHybridExecutive WorkshopBootcampWeekend
Engagement Fit
Engagement fit
Implementation-focused labsPrivate cohort deliveryAdvanced practitioner depthBusiness outcome alignment
Enterprise Customization
Enterprise customization
Tailor this program to your organization's priorities: Helps teams reduce model training time and use multi-GPU hardware more effectively.
- •Align to your framework and model class
- •Add performance-tuning and hardware planning
Credentials
Certification & official source
Aligned to the official source referenced for this program.
View Official SourceResources
Program resources
Yes. Most enterprise clients prefer private delivery scoped to role mix, timezone, and rollout timeline. We align lab environments and scenarios to your tenant context where applicable.
Enterprise Proof
Trusted delivery outcomes
Retail & E-commerce
Representative Retail Analytics Team
Instead of treating reporting as a tooling issue alone, the work focused on consistency, governance, and shared delivery practices across analysts and engineering teams.
- Higher consistency in report design practices
- Improved collaboration between analysts and engineering teams
Healthcare
Representative Healthcare Product Team
The engagement helped product and engineering stakeholders move from interest in AI to clearer implementation choices, security expectations, and prototyping discipline.
- Stronger alignment between product and engineering teams
- Improved clarity on prototype-to-production requirements
Delivery Capability
Enterprise-grade instruction
MCT-led delivery
Programs led by Microsoft Certified Trainer practitioners
Enterprise program oversight
Founder-led specialist delivery with structured rollout planning
Global delivery
APAC · EMEA · Americas · Virtual & Onsite
Implementation-focused
Hands-on labs aligned to production scenarios
