top of page
server-parts.eu

server-parts.eu Blog

NVIDIA DGX B200 8-GPU Servers with NVIDIA Blackwell GPUs: Special Stock Offer

  • Aug 12
  • 5 min read

We have NVIDIA DGX B200 servers with 8× NVIDIA Blackwell GPUs, 1,440GB total GPU memory, dual Intel Xeon Platinum 8570 processors, 2TB system memory, NVMe storage, NVIDIA NVSwitch, ConnectX-7 networking, BlueField-3 DPUs, and NVIDIA AI software stack in stock, ready to ship.


NVIDIA DGX B200 8-GPU Server

Limited Stock At Special Pricing



NVIDIA HGX B200 8-GPU Server, NVIDIA HGX B200, HGX B200 GPU server, NVIDIA Blackwell B200, 8-GPU AI server, enterprise AI server, GPU computing platform, high-performance computing, HPC server, AI infrastructure, AI training server, AI inference server, generative AI server, LLM training server, large language model infrastructure, deep learning server, machine learning server, data center GPU server, accelerated computing, NVIDIA GPU platform, Blackwell GPU server, enterprise GPU infrastructure, scalable AI computing, refurbished NVIDIA HGX B200 server, refurbished GPU server, refurbished enterprise server, enterprise IT hardware, data center hardware, server-parts.eu

Configuration Overview: NVIDIA DGX B200 with 8× NVIDIA Blackwell GPUs

The NVIDIA DGX B200 is designed for organizations that need dense GPU compute, large GPU memory capacity, high-bandwidth GPU interconnect, strong CPU support, and enterprise AI infrastructure.


This configuration includes:

  • NVIDIA DGX B200 server model

  • NVIDIA DGX B200 8-GPU system

  • 8× NVIDIA Blackwell GPUs

  • 1,440GB total GPU memory

  • 64TB/s HBM3e GPU memory bandwidth

  • 2× NVIDIA NVSwitch

  • 14.4TB/s aggregate NVLink bandwidth

  • 2× Intel Xeon Platinum 8570 processors

  • 112 CPU cores total

  • 2TB system memory, configurable up to 4TB

  • 2× 1.9TB NVMe M.2 OS storage

  • 8× 3.84TB NVMe U.2 internal storage

  • NVIDIA AI Enterprise

  • NVIDIA Mission Control with NVIDIA Run:ai technology

  • NVIDIA DGX OS / Ubuntu

  • 10U system chassis

  • Three-year Enterprise Business-Standard Support for hardware and software


The system offers a strong foundation for AI model training, large language model workloads, inference, HPC, GPU-accelerated analytics, and enterprise AI infrastructure.



Full Configuration:  NVIDIA DGX B200


Base System: NVIDIA DGX B200 10U Server

Component

Details

Why it matters

Server Model

NVIDIA DGX B200

Identifies the complete NVIDIA AI server platform

GPU Assembly

NVIDIA DGX B200 8-GPU system

Provides dense GPU compute in one integrated system

Rack Units

10U

Helps buyers plan rack space before deployment

Condition

Not specified

Final condition can be confirmed during quotation

Quantity

Not specified

Final stock quantity can be confirmed during quotation

The NVIDIA DGX B200 is suitable for buyers who need an enterprise NVIDIA Blackwell GPU server for AI, HPC, and accelerated computing environments.



GPU Platform: 8× NVIDIA Blackwell GPUs

Component

Details

Why it matters

GPU

8× NVIDIA Blackwell GPUs

Provides dense acceleration for AI and HPC workloads

Total GPU Memory

1,440GB

Supports large AI models, large datasets, and memory-heavy GPU workloads

GPU Memory Bandwidth

64TB/s HBM3e bandwidth

Supports high-throughput GPU data movement

NVIDIA NVSwitch

2× NVIDIA NVSwitch

Supports high-bandwidth GPU-to-GPU communication

NVLink Bandwidth

14.4TB/s aggregate bandwidth

Important for multi-GPU AI training and large model workloads

The 8-GPU NVIDIA Blackwell configuration makes this DGX B200 suitable for large language models, deep learning, multi-GPU training, GPU-accelerated analytics, and enterprise AI infrastructure.



CPUs: 2× Intel Xeon Platinum 8570

Component

Details

Why it matters

CPU Model

2× Intel Xeon Platinum 8570

Provides host compute for the GPU platform

CPU Core Count

112 cores total

Supports CPU-side processing, data preparation, and system orchestration

Base Frequency

2.1GHz

Supports reliable server workload performance

Max Boost Frequency

Up to 4.0GHz

Supports higher frequency operation when required

Processor Configuration

Dual CPU configuration

Helps balance GPU workloads, data handling, and infrastructure tasks

This CPU configuration supports GPU feeding, data movement, workload coordination, and enterprise infrastructure tasks around the NVIDIA DGX B200 platform.



Memory: 2TB System Memory

Component

Details

Why it matters

Installed Memory

2TB system memory

Provides large system memory capacity for AI and HPC workloads

Maximum Configurable Memory

Configurable up to 4TB

Gives buyers a higher memory planning option

Memory Use Case

AI, HPC, and large dataset workloads

Supports data-heavy workloads alongside GPU acceleration

This memory configuration is suitable for AI training pipelines, large datasets, HPC workloads, and enterprise AI applications that require large system memory capacity.



Storage Configuration: NVMe M.2 and NVMe U.2

Component

Details

Why it matters

OS Storage

2× 1.9TB NVMe M.2

Provides fast storage for operating system deployment

Internal Storage

8× 3.84TB NVMe U.2

Provides high-speed local storage for workloads and datasets

Storage Type

NVMe

Supports fast local data access for AI and HPC workloads

Storage Use Case

OS, local datasets, scratch space, and workload storage

Helps support high-performance AI infrastructure planning

The NVMe storage configuration makes this NVIDIA DGX B200 suitable for fast local workload storage, AI datasets, training pipelines, and operating system deployment.



PCIe Expansion

Component

Details

Why it matters

PCIe / Expansion Details

Not separately specified in the provided stock details

Final expansion layout can be confirmed during quotation

Expansion Use Case

Networking, storage, and accelerator-related infrastructure

Useful for data center integration planning

Configuration Status

To be confirmed from final system inspection

Helps buyers verify compatibility before deployment

The expansion configuration should be confirmed before purchase if the system will be integrated with additional adapters, storage controllers, or specific data center connectivity requirements.



Power & Cooling

Component

Details

Why it matters

System Power Usage

~14.3kW max

Helps buyers plan rack power and data center capacity

Operating Temperature

10–35°C / 50–90°F

Helps buyers plan the operating environment

Cooling Configuration

Not specified in the provided stock details

Final cooling details can be confirmed during quotation

Thermal Planning

Required for 10U 8-GPU DGX deployment

Helps buyers prepare rack, airflow, and facility requirements

This power and cooling information is important for data center planning because the NVIDIA DGX B200 is a dense 10U AI server platform with high power and thermal requirements.



Platform Overview: NVIDIA DGX B200


The NVIDIA DGX B200 is designed for organizations that need:

  • 8× NVIDIA Blackwell GPU acceleration

  • 1,440GB total GPU memory

  • 64TB/s HBM3e GPU memory bandwidth

  • NVIDIA NVSwitch and NVLink GPU interconnect

  • Dual Intel Xeon Platinum host processors

  • 2TB system memory, configurable up to 4TB

  • NVMe M.2 OS storage

  • NVMe U.2 internal workload storage

  • NVIDIA ConnectX-7 networking

  • NVIDIA BlueField-3 DPU support

  • NVIDIA AI Enterprise software

  • NVIDIA DGX OS / Ubuntu

  • 10U enterprise AI server deployment


This server platform is a good fit for AI infrastructure teams, research groups, data center buyers, and enterprise IT teams looking to deploy NVIDIA Blackwell GPU systems for accelerated computing.



Use Cases: NVIDIA DGX B200 with 8× NVIDIA Blackwell GPUs


  • AI model training

  • Large language model workloads

  • Deep learning infrastructure

  • AI inference workloads

  • High-performance computing

  • GPU-accelerated analytics

  • Research computing

  • Enterprise AI platform deployment

  • Distributed training environments

  • Data center GPU infrastructure

  • Multi-GPU workload development

  • AI infrastructure standardization


The NVIDIA DGX B200 configuration makes this server useful for organizations that need dense GPU compute, high GPU memory capacity, strong NVLink bandwidth, large system memory, and high-speed networking in a 10U platform.



Testing, Condition, and Warranty: NVIDIA DGX B200 8-GPU Server


Condition
  • Stock server configuration

  • Hardware details based on available configuration information

  • Final power, cooling, PCIe, and storage layout details can be confirmed during quotation


Testing Includes
  • GPU platform inspection

  • CPU, memory, and system checks

  • Flash storage verification

  • PCIe / expansion configuration inspection where applicable

  • Power and cooling configuration inspection where applicable

  • Basic server hardware validation


Warranty
  • Warranty information available on request

  • Additional support options may be available depending on stock and configuration

  • Final warranty terms confirmed during quotation



FAQ: NVIDIA DGX B200 8-GPU Server


What is the NVIDIA DGX B200 used for?

The NVIDIA DGX B200 is used for AI training, large language models, deep learning, AI inference, HPC, GPU-accelerated analytics, research computing, and enterprise AI infrastructure.


How many GPUs are included in the NVIDIA DGX B200?

This NVIDIA DGX B200 configuration includes 8× NVIDIA Blackwell GPUs with 1,440GB total GPU memory and 64TB/s HBM3e GPU memory bandwidth.


What CPUs are included in this NVIDIA DGX B200 server?

This configuration includes 2× Intel Xeon Platinum 8570 processors with 112 total CPU cores, 2.1GHz base frequency, and up to 4.0GHz max boost.


What storage is included in this NVIDIA DGX B200?

This NVIDIA DGX B200 includes 2× 1.9TB NVMe M.2 drives for OS storage and 8× 3.84TB NVMe U.2 drives for internal storage.


Where can I buy NVIDIA DGX B200 servers in Europe?

You can request a quote from server-parts.eu for NVIDIA DGX B200 servers, NVIDIA Blackwell GPU servers, 8-GPU AI servers, DGX AI systems, HPC servers, and related data center hardware.



NVIDIA DGX B200 8-GPU Server 

Limited Stock At Special Pricing




Comments


bottom of page