GPU Infrastructure & DGX Fundamentals
Understand and operate AI infrastructure: GPUs, DGX systems, networking, scheduling and monitoring for AI workloads.
- ✓ Instructor-led live sessions, online or in Bengaluru
- ✓ Hands-on labs in every module
- ✓ Capstone project with trainer review
- ✓ Curriculum customisable for corporate teams
- ✓ Certificate of completion
Next batch: Sat, 21 Nov 2026 · 24 hours · 3 days
GPU Infrastructure & DGX Fundamentals
Duration: 24 hours · 3 days
Next batch: Sat, 21 Nov 2026
Format: Online live · Bengaluru classroom
Modules: 7 · Topics: 34
Level: Intermediate
Fee: ₹10,999
Trusted by leading experts
Upcoming batches
Open batches for individuals; corporate batches on your dates.
| Programme | Starts | Mode | Schedule | Duration | |
|---|---|---|---|---|---|
| GPU Infrastructure & DGX Fundamentals | Sat, 21 Nov 2026 | Online live | Weekend (Sat) | 4 Saturdays | Enquire |
| GPU Infrastructure & DGX Fundamentals | Mon, 04 Jan 2027 | Bengaluru classroom | Weekday | 3 days | Enquire |
| GPU Infrastructure & DGX Fundamentals | Sat, 13 Feb 2027 | Online live | Weekend (Sat) | 4 Saturdays | Enquire |
Soaring demand, and where it takes you
Organisations investing in on-premise and cloud GPU capacity need infrastructure teams who understand how to run it well.
Become an expert
Roles this programme prepares you for:
Become a AI Infrastructure Engineer
Deploys and operates GPU clusters.
- Explain why AI workloads need accelerated computing
- Describe GPU and DGX system architecture, including NVLink and NVSwitch
- Plan networking and storage for AI clusters
About the course
A foundation in AI infrastructure for platform and operations teams: GPU and DGX system architecture, NVLink and NVSwitch, networking and storage for AI clusters, sharing GPUs with MIG, scheduling with Kubernetes and Slurm, and monitoring with nvidia-smi and DCGM.
Understand and operate AI infrastructure: GPUs, DGX systems, networking, scheduling and monitoring for AI workloads.
Why should I attend?
Infrastructure and data centre engineers
Platform and Kubernetes administrators
HPC administrators
IT operations teams supporting AI
Prerequisites
Linux administration basics; networking fundamentals
Level
Intermediate
Delivery modes, duration and fee
Online live
Instructor-led virtual classroom; weekday (full-day) or weekend (Saturday) batches
Bengaluru classroom
In-person batches at our Bengaluru training venue
On-site at your office
Private corporate batch anywhere in India, scheduled around your teams
Blended
Live sessions plus guided self-paced labs and mentor check-ins
Duration: 24 hours · 3 days
Course fee (open batch): ₹10,999
Corporate / team batches: per-participant or flat batch pricing on request, based on group size, format and customisation.
What will you learn
- Explain why AI workloads need accelerated computing
- Describe GPU and DGX system architecture, including NVLink and NVSwitch
- Plan networking and storage for AI clusters
- Share GPUs with Multi-Instance GPU
- Schedule GPU workloads with Kubernetes and Slurm
- Monitor and troubleshoot GPUs with nvidia-smi and DCGM
Skills you will gain
Architecture
Cluster operations
Monitoring
Tools and platforms covered
Kubernetes
Linux
PrometheusCourse curriculum
M01 AI workloads and infrastructure
- Training vs inference needs
- Accelerated computing
- Cloud vs on-premise
- Power and cooling
- Reference architectures
🧪 Hands-on: Size a workload
M02 GPU and DGX architecture
- GPU components
- DGX systems
- NVLink and NVSwitch
- Tensor Cores
- GPU memory types
🧪 Hands-on: Inspect a GPU system
M03 Networking and storage
- InfiniBand and Ethernet
- Storage for AI
- Topology
- RDMA and GPUDirect
- Parallel file systems
🧪 Hands-on: Design a small cluster
M04 Sharing and scheduling
- Multi-Instance GPU
- Kubernetes GPU Operator
- Slurm
- Time-slicing
- Quotas and fair share
🧪 Hands-on: Configure MIG and schedule jobs
M05 Monitoring and troubleshooting
- nvidia-smi
- DCGM metrics
- Common failures
- Health checks
- Log collection
🧪 Hands-on: Build a GPU dashboard
M06 Operations practices
- Capacity planning
- Upgrades and drivers
- Security
- Firmware lifecycle
- Access control
🧪 Hands-on: Write an operations runbook
M07 Capstone: GPU platform plan
- Workload sizing
- Architecture choice
- Operations model
- Review
🧪 Hands-on: Produce a sized GPU platform design with an operations runbook
Certification preparation
This course covers topics that can help participants prepare for the NVIDIA-Certified Associate: AI Infrastructure and Operations (NCA-AIIO) and NVIDIA-Certified Professional: AI Infrastructure (NCP-AII) exam, which is run by NVIDIA. Check the current exam guide on nvidia.com. Passing is not guaranteed and the exam fee is not included unless stated. [confirm]
Hands-on projects
GPU cluster plan
A documented cluster design and operations runbook for a given AI workload.
Why train with us
Built around your stack
Labs and examples adapted to the tools, cloud and data your team actually uses.
Practice-first
Every module ends in a lab, and the course ends with a reviewed capstone.
Practitioner-led
Delivered by Karthick Selvam and vetted specialist trainers from our network.
Visible progress
Attendance, lab completion and assessment results shared with your L&D team.
Certificate of completion
Participants who complete the labs and capstone receive a Learning Dots certificate of completion for GPU Infrastructure & DGX Fundamentals. [confirm certificate wording / sample image]
Learner reviews
“[Verified learner review for this course]”
“[Verified learner review for this course]”
Meet your trainer
Karthick Selvam
Founder · Data Engineering Corporate Trainer, Bengaluru
[Trainer bio, experience and areas of expertise]
Frequently asked questions
Do participants need DGX hardware?
No. Concepts apply broadly; labs use available GPU systems or cloud instances.
Can the curriculum be customised for our team?
Yes. Modules, depth, duration and labs can be tailored to your stack and goals.
What delivery formats are available?
Online live instructor-led sessions, Bengaluru classroom, or on-site at your office.
How do we enquire or book?
Use the enquiry form on this page or the Enquire button next to any batch.
What does it cost?
The open-batch fee is ₹10,999 per participant plus GST. Corporate batches are priced on group size, format and customisation.
How long is the course?
24 hours · 3 days. Corporate batches can be split into half-day sessions.
A closer look
About this course
A foundation in AI infrastructure for platform and operations teams: GPU and DGX system architecture, NVLink and NVSwitch, networking and storage for AI clusters, sharing GPUs with MIG, scheduling with Kubernetes and Slurm, and monitoring with nvidia-smi and DCGM.
What is GPU Infrastructure & DGX Fundamentals?
Understand and operate AI infrastructure: GPUs, DGX systems, networking, scheduling and monitoring for AI workloads.
Who this course is for
Infrastructure and data centre engineers, Platform and Kubernetes administrators, HPC administrators, IT operations teams supporting AI.
What you will be able to do
Explain why AI workloads need accelerated computing. Describe GPU and DGX system architecture, including NVLink and NVSwitch. Plan networking and storage for AI clusters. Share GPUs with Multi-Instance GPU. Schedule GPU workloads with Kubernetes and Slurm. Monitor and troubleshoot GPUs with nvidia-smi and DCGM.
Career paths
Roles this course supports include AI Infrastructure Engineer, GPU Platform Administrator, Data Centre Architect. Organisations investing in on-premise and cloud GPU capacity need infrastructure teams who understand how to run it well.
NVIDIA & GPU Computing guides
What Is CUDA?
CUDA is NVIDIA's parallel computing platform and programming model for running general-purpose code on NVIDIA GPUs.
What Is NVIDIA RAPIDS?
RAPIDS is a suite of open-source libraries from NVIDIA for GPU-accelerated data science and analytics.
What Are NVIDIA NIM and NeMo?
NIM and NeMo are parts of NVIDIA's software stack for generative AI: NIM for serving models, NeMo for building and customising them.
TensorRT, TensorRT-LLM and Triton Explained
Three NVIDIA technologies for fast, scalable AI inference.