NVIDIA DGX H200 AI System (8x H200 Tensor Core GPU, 1.1TB HBM3e)

The NVIDIA DGX H200 is a purpose-built AI supercomputing system integrating eight NVIDIA H200 Tensor Core GPUs interconnected via fifth-generation NVLink, delivering a combined 1.1 TB of HBM3e memory with 3.35 TB/s of aggregate memory bandwidth. Built on the same Hopper GPU architecture as the H100, the H200 advances the platform with significantly higher memory capacity and bandwidth, enabling larger models to reside entirely in GPU memory and dramatically reducing data movement bottlenecks during training and inference. The system is engineered for maximum out-of-the-box performance, arriving as a fully integrated, validated, and thermally optimized node ready for immediate deployment into enterprise data centers and AI infrastructure environments.

At its core, each H200 GPU delivers up to 3.958 TFLOPS of FP8 Transformer Engine performance and 989 PFLOPS at FP8 precision per GPU, with the full eight-GPU system delivering leadership-class throughput for large language model training, fine-tuning, and inference serving. NVLink Switch fabric provides 900 GB/s of bidirectional bandwidth between GPUs, treating all eight accelerators as a unified memory and compute pool. The DGX H200 includes dual Intel Xeon Scalable processors, 2 TB of system DRAM, high-throughput NVMe storage, and eight ConnectX-7 400GbE/NDR InfiniBand network adapters supporting scale-out cluster configurations.

The DGX H200 is targeted at organizations running frontier-scale generative AI workloads, scientific simulation, drug discovery pipelines, and real-time inference at enterprise scale. NVIDIA ships the system with DGX OS (an optimized Ubuntu-based software stack), the Base Command software suite for cluster orchestration, and access to the NGC container registry providing pre-optimized frameworks including PyTorch, TensorFlow, and RAPIDS. Its high memory capacity makes it particularly suited for training and serving models with hundreds of billions of parameters without model parallelism overheads that constrain lower-memory systems.

Ideal for

• Training and fine-tuning large language models (LLMs) with 70B–700B+ parameters using full model weights resident in unified GPU HBM3e memory
• High-throughput generative AI inference serving for enterprise conversational AI, code generation, and multimodal applications requiring low-latency responses at scale
• Computational drug discovery and molecular dynamics simulation leveraging GPU-accelerated physics engines and deep learning-based protein structure prediction
• Large-scale computer vision and multimodal model development for autonomous systems, medical imaging analysis, and industrial inspection pipelines
• Climate and weather modeling, seismic processing, and scientific HPC workloads requiring both FP64 double-precision accuracy and AI-accelerated surrogate modeling
• Retrieval-augmented generation (RAG) and enterprise AI platform infrastructure where high memory bandwidth reduces embedding and vector search latency at production scale

Technical specifications

Available from Omnixon Global. Submit an RFQ and our team will confirm configuration and availability for your order.