NVIDIA H200 vs H100: Choosing the Right GPU for Massive LLM Inference

Startup founders focused on AI products face a critical decision in selecting between NVIDIA's H100 and H200 GPUs, balancing factors like throughput, memory capacity, and cost-efficiency. The H100 is widely regarded as the standard for large language model training and inference due to its FP8 precision and 80 GB HBM3 memory, making it ideal for training mid-to-large scale models and high-throughput inference. However, the newer H200 GPU offers significant upgrades, including 141 GB of HBM3e memory and increased bandwidth, which enhance its capability for inference-heavy workloads and hosting large models. While the H200's larger memory capacity reduces latency and increases token output, its availability is limited and costs are higher. The H100 remains a practical choice for models under 30 billion parameters, while the H200 is recommended for scaling large inference services where latency and context size are crucial.