Advanced GPU Optimization: How can I tech an LLM with CUDA and ROCm? - Part 3

Welcome back to the final (for now) chapter of our GPU optimization saga! In Part 1, we mastered single-GPU matrix multiplication and built the transformer block. In Part 2, we implemented backpropagation, the AdamW optimizer, and mixed-precision training.

But here is the harsh reality: a single GPU—even an H100 or an MI300X—can barely hold a 70B parameter model in VRAM, let alone train it within a human lifetime. To train state-of-the-art LLMs, we must span hundreds or thousands of GPUs.

In this third part, we will tear down the walls of a single device and venture into the world of distributed training. We will implement All-Reduce for data parallelism, explore Ring-AllReduce algorithms, dive into ZeRO (Zero Redundancy Optimizer) sharding, and write real HIP/C++ code using NCCL (NVIDIA) and RCCL (AMD) to synchronize gradients across nodes.

By the end of this part, you will understand:

How collective communication (All-Reduce) works under the hood.