Lilly Tech Systems
GPU Cloud Computing
Master GPU hardware and cloud instance selection for AI workloads. Understand NVIDIA and AMD GPU architectures, navigate cloud instance types, configure multi-GPU training, and apply best practices for maximizing GPU performance and cost efficiency in the cloud.
What You'll Learn
Deep technical understanding of GPU hardware and cloud configuration for AI.
NVIDIA GPUs
H100, A100, L4, T4 architectures, Tensor Cores, NVLink, and CUDA ecosystem.
AMD GPUs
MI300X, CDNA architecture, ROCm software stack, and cloud availability.
Instance Types
Navigate GPU instance families across AWS, GCP, and Azure for every workload.
Multi-GPU
Data parallelism, model parallelism, pipeline parallelism, and FSDP for distributed training.
Course Lessons
Follow the lessons to build deep GPU cloud computing expertise.
1. Introduction
Why GPUs dominate AI computing, CPU vs GPU architecture, and the GPU cloud landscape.
2. NVIDIA GPUs
H100, A100, L4, T4 deep dive: Tensor Cores, memory hierarchy, NVLink, and CUDA.
3. AMD GPUs
MI300X, CDNA 3 architecture, ROCm ecosystem, and when to choose AMD over NVIDIA.
4. Instance Types
Complete guide to GPU instances across AWS, GCP, and Azure with cost-performance analysis.
5. Multi-GPU
Distributed training strategies: data parallelism, model parallelism, FSDP, and DeepSpeed.
6. Best Practices
GPU utilization optimization, memory management, profiling, and production operations.
Prerequisites
What you need before starting this course.
- Basic understanding of cloud computing (instances, storage, networking)
- Familiarity with Python and deep learning frameworks (PyTorch or TensorFlow)
- General understanding of neural network training and inference
- Command-line proficiency for cloud instance management