AMD vs NVIDIA for AI Inference: MI300X vs H100 Explained

AMD and NVIDIA build AI inference hardware on two very different ideas. NVIDIA bets on compute density and fast GPU-to-GPU links. AMD bets on huge memory per GPU. This video breaks down what that means for real workloads. In this video: AMD Instinct (MI300X, MI325X, MI350X) vs NVIDIA (H100, H200, B200) Why prefill is compute-bound and decode is memory-bound How KV cache growth causes out-of-memory errors and evictions Tensor parallelism (TP=1 vs TP=2/4/8) and AllReduce overhead CUDA and TensorRT-LLM vs ROCm, vLLM and Triton Cost per million tokens and what it means for deployment. SOURCE Runpod Cloud GPUs: https://www.runpod.io/product/cloud-gpus Figures such as pricing differences and bandwidth come from the source material and vary by vendor, configuration and workload. Always benchmark your own models before buying or renting. This video was created with NotebookLM from the source material above.