NVIDIA DGX H800
- Condition
- New
- Manufacturer
- NVIDIA
- CPU
- 2x Intel Xeon Platinum 8480C (56C 105M Cache 2.00 GHz)
- RAM
- 32x 64GB DDR5 4800MHz
- Power supply unit
- 6x3000W Titan
- RAID controller
- 9560-16iRAID
- HDD
- 8x3.84T NVMe PCIe/ 2x960Gb NVMe M.2
- NIC card
- 2xNVIDIA InfiniBand 400Gb/ 8xNVIDIA ConnectX-7 MCX75310AAS-NEAT400G
- GPU
- NVIDIA DELTA-NEXT Vulcan SXM5 8xNVIDIA H100 640G
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Submit a requestNVIDIA DGX H800 is an integrated accelerated computing system designed for enterprise artificial intelligence, machine learning, generative AI, analytics, and high-performance computing (HPC) workloads. Built around eight NVIDIA H800 Tensor Core GPUs, the platform provides a unified architecture for AI training, inference, and large-scale data processing in modern data centers.
Unlike a traditional GPU server assembled from individual components, NVIDIA DGX H800 combines GPU acceleration, high-speed interconnects, storage, networking, and a validated NVIDIA software stack in a single enterprise-ready platform. It can operate as a standalone AI compute node or serve as a building block for scalable DGX-based clusters.
GPU architecture for AI training and inference
NVIDIA DGX H800 is equipped with eight NVIDIA H800 Tensor Core GPUs based on the NVIDIA Hopper architecture. Each GPU provides 80GB of memory, giving the system 640GB of total GPU memory for large AI models, generative AI workloads, accelerated analytics, and HPC applications.
The NVIDIA H800 Tensor Core GPU is designed for demanding AI workloads, including large language model training, fine-tuning, inference, recommendation systems, computer vision, analytics, and scientific computing. Tensor Cores with support for FP8, BFLOAT16, FP16, TF32, and INT8 acceleration help improve throughput across training and inference pipelines.
The H800 SXM GPU supports NVIDIA NVLink connectivity at up to 400GB/s and PCIe 5.0 bandwidth at up to 128GB/s. In a DGX H800 system, these accelerators are integrated into a multi-GPU platform that supports high-speed intra-node communication and efficient execution of distributed AI workloads.
The system uses a dual Intel Xeon Platinum 8480C processor platform and 2TB of system memory, supporting data preparation, orchestration, containerized workloads, and CPU-side processing tasks required by enterprise AI pipelines.
Primary use cases
- AI model training and fine-tuning. DGX H800 accelerates development and optimization of large-scale AI models, including generative AI, natural language processing, computer vision, recommendation systems, and enterprise AI applications.
- Large-scale inference workloads. The platform is designed for deploying production AI services, intelligent applications, automated decision systems, chatbots, and real-time model execution.
- Data analytics and accelerated computing. DGX H800 supports GPU-accelerated analytics, scientific computing, simulations, and workloads that combine traditional HPC methods with machine learning.
- Multi-user AI environments. NVIDIA Multi-Instance GPU (MIG) technology allows each H800 GPU to be partitioned into up to seven isolated GPU instances with dedicated memory and compute resources, improving resource utilization for multiple teams and applications.
- Enterprise AI infrastructure. DGX H800 provides a standardized foundation for organizations building internal AI platforms, research environments, scalable machine learning operations, and private generative AI infrastructure.
NVIDIA DGX H800 in enterprise infrastructure
NVIDIA DGX H800 is designed for organizations that require dedicated AI computing resources for continuous development, testing, and production deployment. The platform enables data science teams, AI engineers, and enterprise developers to work within a consistent hardware and software environment instead of managing separate accelerator servers and integration layers.
The system can be deployed as an individual AI node or integrated into larger NVIDIA-based environments. When planning an enterprise deployment, organizations should evaluate not only GPU performance but also network architecture, external storage requirements, workload distribution, power availability, cooling capacity, and data center readiness.
With its integrated hardware design, NVIDIA software ecosystem, and support for scalable AI deployments, DGX H800 helps enterprises shorten the path from AI experimentation to production use. The platform provides a reliable foundation for organizations developing generative AI services, machine learning platforms, analytics solutions, and HPC applications that require predictable performance and simplified infrastructure management.
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