CUDA and GPU Programming for AI Workloads Corporate Training
Give your engineering team the GPU programming skills that speed up AI training and inference.
Build the GPU-accelerated AI skills your engineering teams need. Edstellar's CUDA & GPU Programming for AI Workloads corporate training program helps your software engineers and AI developers master CUDA programming, GPU memory optimization, and parallel computing through instructor-led sessions with hands-on labs on real GPUs. As the corporate training company enterprises trust for emerging-technology programs, we tailor every workshop to your team's stack, hardware, and AI workloads.
Instructor-led group trainingVirtual / on-site / off-site10 languagesDelivered inEnglish, Spanish, Mandarin, German, Arabic, Portuguese, Hindi, French, Japanese, Italian100+ countriesWe have delivered inUnited States, United Kingdom, India, Canada, Germany, France, Netherlands, UAE, Saudi Arabia, Singapore, Australia, Japan, Brazil, South Africa and 90+ more countries
How we build GPU-ready engineering teams
THE EDSTELLAR CUDA & GPU CAPABILITY MODEL
Baseline GPU fluency
Assess how your engineers rate CUDA concepts, kernel writing, and GPU performance basics.
Set one GPU standard
One shared language for CUDA programming, memory optimization, and AI workload acceleration across the team.
Practice on real GPUs
Guided hands-on labs apply CUDA kernels, streams, and profiling to realistic AI workloads.
Prove the speedup lift
Baseline and capstone benchmarks measure each engineer's ability to accelerate an AI workload.
Scale across teams
One GPU programming standard rolls out across engineering cohorts as AI workloads grow.
Need more than one program?Get a group quote
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Program Overview: What is CUDA and GPU Programming for AI Workloads Corporate Training?
CUDA & GPU Programming for AI Workloads is a 24-hour instructor-led program for software engineers and ML practitioners who need AI workloads running at full GPU speed. Across six modules, participants work through GPU architecture and the CUDA programming model, kernels with thread hierarchy and memory spaces, memory optimization with concurrent execution, CUDA libraries with deep learning frameworks, and multi-GPU programming with tensor cores and profiling. It closes with a capstone where each team accelerates an AI workload end to end on the GPU.
Training is built around GPU labs: four practice sessions alternate with instructor walkthroughs, so participants write kernels, map thread hierarchies, tune memory access patterns, exploit concurrent execution, call CUDA libraries from deep learning frameworks, and profile across multi-GPU setups with tensor cores. Each lab runs on real GPUs, with the instructor reviewing kernel code, diagnosing bottlenecks, and coaching the optimization habits that separate fast code from code that merely runs. Peer review sharpens every kernel before it is benchmarked. The program culminates in a four-hour capstone where teams deliver an end-to-end GPU-accelerated AI workload.
AI teams that treat the GPU as a black box pay for it in training bills and inference latency: unoptimized kernels, memory-bound code, and single-GPU thinking leave most of the hardware's throughput unused. Framework defaults only carry a model so far, and the gap between a working pipeline and a fast one is where real costs hide. This program builds the low-level fluency: the CUDA programming model, memory hierarchy mastery, and profiling discipline that turn expensive GPUs into actual speed.
The capstone AI workload acceleration is the program's benchmark: each team takes a realistic AI workload and delivers it GPU-accelerated end to end, with profiled kernel code, optimized memory usage, multi-GPU or tensor core exploitation where it pays, and measured speedups to prove it. The instructor reviews every optimization the way a performance engineer would, so claimed gains get verified on real hardware. Teams return with kernel templates, profiling checklists, and the hands-on fluency to make their own models and pipelines run at the speed their GPUs were bought for.
Across six modules, your engineers move from CUDA foundations to a measured AI workload speedup capstone. Sessions use hands-on labs on real GPUs, profiling with Nsight tools, and your own training or inference workloads, so every technique transfers directly to your enterprise.
Delivered as an instructor-led program in virtual, onsite, or hybrid formats, it fits around your calendar, with scheduling set along with you, and content tailored to your stack and hardware.
What's Included in this Workshop for Your Enterprise Teams
- 1
CUDA foundations
GPU architecture and the CUDA execution model.
- 2
Kernel development
Write, debug, and optimize custom CUDA kernels.
- 3
Memory and streams
Coalescing, shared memory, and concurrent execution.
- 4
Libraries and frameworks
cuBLAS, cuDNN, and GPU-accelerated PyTorch workflows.
- 5
AI workload capstone
Profile and accelerate a real AI pipeline end to end.
- Training delivered at any of your offices globally
- 10+ languages
- Schedule around your calendar
- Group size set along with you
- Run as one cohort or split into batches
- Instructor-led (virtual/onsite/hybrid)
- Recurring and annual options
- Trainer sourcing, matching and logistics
Build your team's skills. Anywhere in the world.
Real-world use cases
from day one
Instructor-led training
for your teams
Delivered globally
in 100+ countries
Enterprise Training Solutions Customized for Your Company
One partner for instructor-led corporate training, delivered the way your teams work.
Request a Training Quote- Customized Training Delivery
- Scale Your Training: Small to Large Teams
- In-person Onsite, Live Virtual or Hybrid Training Modes
- Plan from 2000+ Industry-ready Training Programs
- Hands-On Labs on Live Infrastructure, Led by Industry Experts
CUDA and GPU Programming for AI Workloads Course Topics and Program Outline
Six modules follow a clear learning path from CUDA foundations to an AI workload acceleration capstone. Each session builds on the last, with hands-on labs on real GPUs that turn parallel programming concepts into measured speedups.
Built by Edstellar with GPU and HPC engineers
Built by engineers who write CUDA kernels and ship GPU-accelerated AI workloads in production, shaped around your stack and use cases.
Baseline each participant's GPU programming readiness before module 01 begins
Build strong CUDA and GPU foundations in instructor-led sessions for your team
Reinforce skills through guided hands-on labs on CUDA kernels and GPU profiling
Apply CUDA and GPU skills to real-world scenarios and a capstone optimization project
A short pre-training assessment sets each participant's entry level, so the program starts where your team actually is. The rest is weighted toward hands-on practice: CUDA kernel development and GPU performance profiling labs and realistic scenarios drawn from your team's work, not slides.
Delivered as
Entry level set by the assessment. Depth and schedule customized to your team.
Explore the GPU execution model on a real NVIDIA GPU and run your first kernel to see how thousands of threads map to hardware.
learna)CPU vs GPU execution models and where each wins
b)CUDA threads, blocks, and grids
c)CUDA tooling: NVCC, Nsight, and CUDA toolkit versions
d)Your first kernel: compiling and launching CUDA code
Write and debug custom CUDA kernels, map thread indices to data, and use shared memory to accelerate a matrix transpose.
practicea)Writing kernels: syntax, indexing, and launch configuration
b)Thread blocks, warps, and occupancy
c)Global, shared, constant, and register memory
d)Warp divergence and synchronization with __syncthreads()
e)Debugging kernels: race conditions and memory errors
Optimize a memory-bound kernel for coalesced access, then overlap transfers and compute with CUDA streams and events.
practicea)Memory coalescing and bandwidth measurement
b)Bank conflicts and shared memory patterns
c)CUDA streams, events, and overlapping transfers
d)Unified memory and pinned memory strategies
Accelerate linear algebra with cuBLAS, profile a PyTorch model on GPU, and tune data loading to remove host bottlenecks.
practicea)cuBLAS, cuFFT, cuRAND, and Thrust primitives
b)cuDNN for neural network layers
c)PyTorch and TensorFlow GPU workflows
d)Data pipelines: keeping the GPU fed with batched loading
Profile a kernel with Nsight Compute, apply mixed precision on Tensor Cores, and scale work across two GPUs with NCCL patterns.
practicea)Nsight Compute and Nsight Systems profiling workflow
b)Mixed precision training with Tensor Cores
c)Multi-GPU patterns: NCCL, data parallelism, model sharding
d)Reducing kernel launch overhead and register pressure
e)Scaling inference: batching, quantization, and throughput tuning
Take a real AI training or inference pipeline, profile it end to end, and deliver a measured speedup with a before-and-after report.
applya)Profiling an AI workload end to end
b)Finding and removing the top bottlenecks
c)Custom kernels for inference hot paths
d)Capstone demo: measured speedup and results report
Customize this program tailored to your team?
Tell us your training needs: what your team already works with, the level they’re at, and the topics you want them to focus on. We’ll adjust the modules, duration and hands-on labs, and send you a customized outline for your group.
Key Skills and Outcomes Your Team Will Gain
Your engineers will leave able to write, optimize, and profile CUDA code that speeds up AI training and inference. Each outcome is practiced in hands-on labs and proven in the capstone benchmark.
Write and launch CUDA kernels
Develop custom CUDA kernels with correct thread indexing, block configuration, and synchronization.
Optimize GPU memory usage
Apply coalescing, shared memory, and pinned memory patterns that maximize bandwidth on real hardware.
Run concurrent GPU work
Use CUDA streams and events to overlap data transfers with computation for higher throughput.
Use the CUDA library ecosystem
Accelerate code with cuBLAS, cuFFT, cuRAND, Thrust, and cuDNN primitives.
Accelerate deep learning frameworks
Tune PyTorch and TensorFlow GPU workflows, data loading, and mixed precision training.
Profile and fix GPU bottlenecks
Use Nsight Compute and Nsight Systems to find and remove kernel and memory bottlenecks.
Scale across multiple GPUs
Apply data parallelism, NCCL patterns, and Tensor Core techniques for AI training and inference speedups.
Teams that Benefit from this CUDA and GPU Programming for AI Workloads Group Training
This program is built for engineers who own AI performance: software engineers, AI developers, and ML engineers. Solid C or C++ programming experience is expected; content adapts to each cohort, so engineers practice the GPU techniques their AI workloads actually need.
Master the parallel programming behind modern compute.
Software engineers learn CUDA kernel development, thread hierarchy, and memory optimization, so compute-heavy code runs at full GPU speed.
Workflow friction
- Custom kernels are slow and hard to debug
- Memory access patterns limit throughput
- GPU expertise sits with one or two people
How the program helps
- Write fast, correct CUDA kernels
- Remove memory bandwidth bottlenecks
- Spread GPU skills across the team
Practice use cases
- Kernel development
- Performance tuning
- Code reviews
Cut training time and inference cost.
AI developers learn to profile PyTorch and TensorFlow workloads, apply mixed precision, and scale across GPUs, so models train faster and serve cheaper.
Workflow friction
- GPU training jobs run too long
- Inference latency misses product targets
- Cloud GPU bills keep climbing
How the program helps
- Train models faster on the same hardware
- Hit inference latency targets
- Lower GPU compute costs
Practice use cases
- Model training
- Inference optimization
- Capacity planning
Make GPU infrastructure earn its keep.
Platform teams learn CUDA profiling, multi-GPU patterns, and workload benchmarking, so clusters run hot and shared resources deliver maximum throughput.
Workflow friction
- GPU clusters sit idle between jobs
- No shared standard for GPU performance
- Hard to prove cluster ROI
How the program helps
- Raise GPU utilization across the cluster
- Set performance standards for teams
- Report real throughput gains
Practice use cases
- Cluster tuning
- Benchmarking
- Resource planning
Each cohort works through hands-on labs matched to its roles, then leaves with a benchmarked AI workload speedup for day-to-day work.
Prerequisites: Participants need solid C or C++ programming experience, since CUDA is C++ based and the labs are code-heavy from day one. Familiarity with basic parallel computing concepts and Python-based deep learning frameworks helps, but GPU programming experience is not required. Content adapts to each cohort's stack and hardware, so teams learn on the tools they use every day.
C++ Developer to AI Workload Accelerator
The program takes engineers from CUDA foundations to AI workload acceleration: foundation programming model, intermediate kernel and memory work, then advanced profiling, multi-GPU scaling, and a capstone speedup.
Engineers grow from CUDA basics to end-to-end AI workload acceleration as the modules progress.
Mixed cohort, or several teams at once?
We can tailor the training to different teams, roles, and experience levels within the same program.
Certificate of Training Completion.
On successful completion, employees receive an Edstellar course completion certificate, recognizing their commitment to ongoing learning and professional development. It validates the skills they have built and motivates them to keep growing and contribute to organizational success.
Earned, not attended. Awarded on completion of all hands-on labs and the capstone project.
Training Delivery Modes: Virtual, Onsite and Hybrid
Every mode is shaped around your stack and hardware before delivery. The curriculum, hands-on labs, and capstone stay the same in each format, so what your engineers can do after training never depends on how you run it.
Hybrid Training
Live instruction across office and remote sessions, with hands-on labs coached throughout. Your engineers write kernels, optimize memory, and profile AI workloads with instructor guidance. Best for multi-site teams.
- Live instruction across office and remote sessions
- Hands-on labs with instructor coaching on real GPUs
- Real-world AI workload examples and profiling
- Best for engineers across sites and time zones
Onsite Training
Your instructor delivers at your facility, with labs set up on your GPU hardware. Participants write and profile kernels with direct coaching. Best for co-located teams.
- Instructor delivers at your facility
- Labs set up for your team's GPUs and stack
- Direct coaching during lab work
- Best for co-located teams wanting maximum interaction
Live Virtual Training
Live virtual sessions on Zoom or Microsoft Teams, with remote access to GPU lab environments and screen-shared guidance through every task. Your engineers code and profile in real time. Best for distributed engineering teams.
- Live sessions on Zoom or Microsoft Teams
- Remote access to GPU lab environments
- Screen-shared guidance through every lab
- Best for distributed engineering teams
Need a training format that works for your team?
Full days, half days across weeks, or split by module. Tell us the delivery window and we schedule the cohort around it.
Frequently Asked Questions
Share your team size, preferred dates, and delivery mode in the lead form. We reply with a tailored proposal covering scope, schedule, and pricing for your group.
Both. Choose live virtual sessions on Zoom or Teams, onsite delivery at your office, or a hybrid mix across sites and time zones.
The standard program runs 24 hours of instructor-led sessions, typically over three days. We can reshape the schedule around your calendar.
We recommend 12 to 18 engineers per cohort for strong lab coaching. Larger engineering teams can run as parallel batches.
Yes. We shape the labs, profiling examples, and capstone around your GPU hardware, frameworks, and AI workloads before delivery.
Question not answered here?
Put it in the request. A training specialist answers it directly, within one business day.
Where this Training Fits in Your Team's learning path.
CUDA programming comes after solid C++ and before running GPU infrastructure at scale. Your team needs C++ memory and performance habits to write kernels, then learns how GPU clusters are operated and applies both to make model inference faster and cheaper.
Why Choose Edstellar as Your CUDA and GPU Programming for AI Workloads Enterprise Training Provider
If your engineering team needs faster AI training and inference, start here. Tell us about your stack, GPU hardware, timelines, and preferred delivery mode, and we will shape a program around them.
EXPERT TRAINERS
YEARS DELIVERING
ORGANIZATIONS TRAINED
CERTIFIED
Tailored
Content shaped around your CUDA and GPU programming use cases, so every session maps to your real CUDA needs.
Proven Methodology
5+ years delivering corporate training to software, HPC, and ML engineering teams across industries and regions.
Practice-Based Learning
6 hands-on labs across 6 modules. Your team builds optimized CUDA kernels and profiling workflows with instructor guidance.
Expert Trainers
5,000+ certified trainers matched to your CUDA and GPU stack, with a full trainer profile shared before your program is booked.
Global Scale
One CUDA and GPU program across your offices worldwide, in hybrid, onsite, or live virtual.
Measurable Outcomes
Baseline-to-capstone measurement and Skills Matrix reporting keep your CUDA capability lift visible.
Accelerate AI Workloads Through CUDA Programming and Measured Bottleneck Analysis
Edstellar connects GPU architecture with kernel design, memory optimization, and concurrent execution. Engineers profile representative AI workloads and compare changes across libraries, precision choices, and multi-GPU patterns. The final report emphasizes reproducible measurements and preserved correctness, explaining which bottlenecks changed and what performance limitations remain.
- Step 01
Consult
We assess C or C++ fluency and parallel-computing familiarity to identify gaps in thread mapping, memory behavior, kernel debugging, and GPU profiling.
- Step 02
Plan
We establish CUDA execution and kernels before memory optimization and streams, then libraries, framework profiling, and multi-GPU techniques within the acceleration capstone.
- Step 03
Align
We align GPU programming instruction with code-ready engineers, confirming NVIDIA hardware and tooling appropriate for kernel, profiling, and multi-GPU exercises.
- Step 04
Customize
We tailor matrix operations and AI pipelines to representative workloads while keeping input sizes, hardware conditions, and correctness checks consistent across comparisons.
- Step 05
Execute
Participants debug kernels, improve memory access, and profile training pipelines, receiving feedback on bottleneck evidence, concurrency, numerical choices, and measured performance effects.
- Step 06
Evaluate
We assess the acceleration report for reproducible baselines, verified output correctness, explained bottleneck changes, and justified conclusions about performance and remaining constraints.
Also from Edstellar
Talent assessments
Psychometric, behavioral, leadership and 360-degree assessment.
→Training needs analysis
Needs analysis, gap identification, and a training roadmap.
→L&D consulting
Learning strategy, content, technology and ROI measurement.
→OD consulting
Org design, succession planning and cultural change.
→Managed training services
Training outsourcing, vendor management, logistics and administration.
→Coaching solutions
Executive and manager coaching that keeps skills in use.
Start an RFP shaped to your needs.
Answer three quick questions about team size, scope, and how often you need the program run, then send the request from the last step. Everything you choose travels with it, so you only fill this in once.
How many team members need training?
CUDA & GPU Programming for AI Workloads Training Training Packages & Pricing
Where L&D holds an annual budget, license packages price the whole year at once instead of raising a purchase order per workshop. Hours and licenses are pooled across teams, programs and locations, so unused capacity is not stranded in one department.
Starter
120
trainee licenses64 hours of group training
Tailored for SMBs
Growth
320
trainee licenses160 hours of group training
Ideal for growing SMBs
Enterprise
800
trainee licenses400 hours of group training
Designed for large corporations
Custom
Unlimited
trainee licensesDuration set by the rollout
Multi-region and multi-year
Training Packages & Pricing
Choose the training format that works for your team. Pricing is customized based on your requirements, cohort size, and delivery needs.
Request CUDA and GPU Programming for AI Workloads Training for Your Team
Share your team size, AI workloads, GPU hardware, preferred delivery mode, and timelines. We will respond with a tailored training plan covering scope, schedule, and pricing, and confirm trainer availability.