The NVIDIA HGX H200 is built on the Hopper GPU architecture, delivering strong performance, high memory capacity, and efficient multi-node scaling for modern AI workloads. It is a production-proven platform with a deep ecosystem of software and tooling support.
Choose from validated HGX H200 platforms built for production AI. Each system is ready to deploy and optimized for high-density GPU environments.
At the core of each system is the NVIDIA HGX H200 8-GPU baseboard. It is the foundation of Hopper-generation AI infrastructure, built to deliver consistent, high-throughput compute for training and inference workloads at scale.
Eight NVIDIA Hopper GPUs per node, providing the compute density needed for large-scale AI training and production inference.
Large memory capacity per GPU, designed to handle the demands of large models and datasets without bottlenecking.
Fast GPU-to-GPU communication for efficient distributed training and minimal overhead across multi-node configurations.
Built for 24/7 sustained operation in production environments with enterprise reliability requirements.
Train large-scale models, fine-tune LLMs, and run distributed training jobs across multi-node configurations. The H200 platform delivers the memory capacity and interconnect bandwidth to keep GPU utilization high throughout long training runs.
Serve models in production with predictable throughput and low latency. The HGX H200 handles real-time inference for LLMs, vision models, and multi-modal pipelines with the memory headroom to support large context windows and batch sizes.
Run compute-intensive simulations, molecular dynamics, climate modeling, and other HPC workloads that benefit from dense GPU compute and high memory bandwidth.
