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NVIDIA H100 8x80GB Baseboard

Price range: 10€ through 100€

(excl. TAX)

Delivery is made within 14-21 days

Pricing for

Nvidia

NVIDIA H100 8×80GB Baseboard. 8×H100 80GB SXM for state-of-the-art LLM performance. Direct import, vendor warranty, pro packaging, fast door-to-door delivery, any payment.

Product description

8×NVIDIA H100 80GB SXM GPU Baseboard: Ultimate AI and HPC Power

8×NVIDIA H100 SXM 80GB GPU Baseboard is a high-performance server platform featuring eight NVIDIA H100 GPUs with 80 GB of HBM3 memory each. Together, they provide an impressive 640 GB of ultra-fast memory and supercomputer-level compute power for the most demanding AI and high-performance computing (HPC) workloads.

Designed for modern data centers and AI clusters, this baseboard connects all GPUs via NVLink and NVSwitch, delivering inter-GPU bandwidths of up to hundreds of GB/s. Unlike PCIe-based setups, all accelerators operate as a unified compute fabric, eliminating bottlenecks and ensuring maximum scalability.

Specifications

  • Series: Tesla
  • GPU Architecture: Hopper
  • Total Memory: 640 GB HBM3
  • Memory per GPU: 80 GB HBM3
  • CUDA Cores: 135,168
  • Tensor Cores: 4,224 (4th Gen)
  • Theoretical Performance: up to 535 TFLOPS (FP16/FP8 Tensor)
  • Interconnect: NVLink + NVSwitch (up to 900 GB/s per GPU)
  • Interface: PCIe 5.0 x16 (for baseboard integration)
  • Form Factor: HGX SXM5
  • Power Consumption: approx. 5600 W (8 GPUs combined)
  • Cooling: Liquid-cooled or advanced air-cooled solutions (used in Cray, HPE, and OEM systems)

Advantages of Baseboard vs. Separate GPUs

  • Unified architecture. All eight GPUs act as a single computational system — essential for training large language models (LLMs) that must fit entirely into memory.
  • Extreme interconnect speed. NVSwitch enables direct GPU-to-GPU data exchange at up to hundreds of GB/s — far beyond the limits of PCIe.
  • Infrastructure efficiency. Shared cooling, power, and communication modules significantly reduce energy usage and simplify integration.
  • Cost optimization. In real-world training and inference time, SXM modules achieve higher ROI compared to multiple PCIe GPUs.

Applications

  • AI and LLM training. Large-scale neural networks such as GPT, DeepSeek, Qwen, and Mistral.
  • HPC simulations. Molecular dynamics, physics, energy systems, and climate modeling.
  • Generative AI. Training multimodal and large generative models requiring high data throughput.
  • Cloud infrastructure. Scalable GPU clusters for enterprise and research workloads.

Why Choose NVIDIA H100 80GB SXM Baseboard

  • Eight H100 GPUs in one unit — providing supercomputer-class compute density.
  • NVLink/NVSwitch architecture ensures performance impossible to achieve with PCIe solutions.
  • OEM design offers DGX-equivalent architecture at a more accessible cost.
  • Future-ready platform for scaling next-generation AI clusters and data centers.

8×NVIDIA H100 SXM 80GB GPU Baseboard is the foundation for next-generation AI infrastructure — delivering maximum speed, scalability, and reliability. Ideal for organizations developing advanced LLMs, generative AI, and HPC workloads requiring unmatched computational performance.

Additional information

Weight 1,8 kg
Dimensions 26,7 × 11,1 cm
Country of manufacture

Taiwan

Manufacturer's warranty (years)

1

Model

NVIDIA H100

Cache L2 (MB)

50

Process technology (nm)

4

Memory type

HBM3

Graphics Processing Unit (Chip)

Number of CUDA cores

16896

Number of Tensor cores

432

GPU Frequency (MHz)

1590

GPU Boost Frequency (MHz)

1980

Video memory size (GB)

80

Memory frequency (MHz)

18000

Memory bus width (bits)

5120

Memory Bandwidth (GB/s)

3350

Connection interface (PCIe)

PCIe 5.0 x16

FP16 performance (TFLOPS)

1979

FP32 performance (TFLOPS)

989

FP64 performance (TFLOPS)

49

Cooling type

Passive (server module)

Number of occupied slots (pcs)

8

Length (cm)

26.7

Width (cm)

11.1

Weight (kg)

1.8

Temperature range (°C)

0–85

Multi-GPU support

Yes, via NVLink

Virtualization/MIG support

MIG (up to 7 instances)

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