Advanced GPU Systems & AI Acceleration
Engineer the GPU systems AI teams run on — async CUDA pipelines, multi-GPU, Tensor Cores, DL/LLM acceleration, HPC patterns, and production inference infrastructure. 10 modules, labs throughout, benchmarked capstone. Pathway Step 4.
4.9
(305 students)
4 Weeks · 54 hours
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What is this course about?
Advanced GPU Systems & AI Acceleration
Multi-GPU · Tensor Cores · LLM Acceleration
Scale CUDA systems for AI—multi-GPU patterns, Tensor Cores, mixed precision, and inference acceleration—built for engineers shipping GPU-backed ML.
What you'll walk away with:
- Multi-GPU and advanced CUDA systems fluency
- Tensor Core and mixed-precision skills for AI workloads
- LLM/inference acceleration patterns on NVIDIA GPUs
- Performance evidence habits for latency and throughput SLAs
- Capstone-ready skills for GPU systems and AI infra roles
Course Features
Post Graduate Diploma
4 Weeks of Content
Hands-on Projects
Community Support
Lifetime Access
Student Reviews
Olivia Hartmann
Multi-GPU chapter finally made NVLink vs PCIe trade-offs click. Our team’s load-balancing doc now cites the same topology diagrams from the labs.
Munich Automotive Sim LabDev Shah
LLM systems week paid for itself—KV cache and continuous batching section mapped 1:1 to our vLLM tuning postmortems.
Hyd ML PlatformChen Wei
Tensor Core + mixed precision block gave me vocabulary to defend BF16 choices to our training lead. Benchmark tables from the course template now ship with every PR.
Singapore Quant AIAisha Okonkwo
Dynamic parallelism module unlocked nested algorithms we had been hacking on CPU. Device-side launch semantics section prevented a subtle sync bug.
Lagos Fintech GPU DeskTomás Rivas
Interoperability chapter (Vulkan + external memory) was the missing link for our viz+compute pipeline. Shared-buffer flow is now documented for new hires.
CDMX Real-Time Graphics StudioKaito Mori
Chapter 10 capstone read like an infra design review—benchmark harness, failure cases, deployment sketch. Hiring manager called out the SLA narrative specifically.
Tokyo Edge Inference Co.