“We needed a partner who understood both the technical depth of Azure OpenAI and the governance requirements of an enterprise.”
Enterprise Program Brief
Track: NVIDIA DLI Certificate of Competency
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
Distributed TrainingAI
Data Parallelism: How to Train Deep Learning Models on Multiple GPUs
NVIDIA Deep Learning
NVIDIA
On this page
Ideal for
Deep Learning PractitionerDistributed TrainingTailored Team DeliveryImplementation-Focused
How VNode delivers this
Who this is for, and how we run it
VNode ITeS delivers Data Parallelism: How to Train Deep Learning Models on Multiple GPUs as an MCT-led NVIDIA program for Deep Learning Practitioner. Typical duration is 8 hours (Virtual, On-site, or Hybrid). Labs follow production-shaped scenarios rather than slide-only walkthroughs. Private cohorts can shift emphasis by role mix, workspace or repo constraints, and rollout timing.
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
- Relevant foundational experience in the target technology area.
- Comfort with hands-on labs in a cloud or GPU-accelerated environment.
Program Outcomes
Capabilities your teams will gain
Strengthen capability in distributed training scenarios
Strengthen capability in nvidia deep learning scenarios
Strengthen capability in deep learning scenarios
Curriculum
Curriculum roadmap
1
Distributed Training
2
NVIDIA Deep Learning
3
Deep Learning
1Module 1
Distributed Training
+
Module 1
Distributed Training
Cover distributed training skills and implementation practices aligned to Data Parallelism: How to Train Deep Learning Models on Multiple GPUs.
- Cover distributed training skills
- implementation practices aligned to Data Parallelism: How to Train Deep Learning Models on Multiple GPUs
2Module 2
NVIDIA Deep Learning
+
Module 2
NVIDIA Deep Learning
Cover nvidia deep learning skills and implementation practices aligned to Data Parallelism: How to Train Deep Learning Models on Multiple GPUs.
- Cover nvidia deep learning skills
- implementation practices aligned to Data Parallelism: How to Train Deep Learning Models on Multiple GPUs
3Module 3
Deep Learning
+
Module 3
Deep Learning
Cover deep learning skills and implementation practices aligned to Data Parallelism: How to Train Deep Learning Models on Multiple GPUs.
- Cover deep learning skills
- implementation practices aligned to Data Parallelism: How to Train Deep Learning Models on Multiple GPUs
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
- •NVIDIA DLI Certificate of Competency
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