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NVIDIA Tesla V100 32 GB HBM2 SXM2 module with passive cooler

NVIDIA

NVIDIA Tesla V100 32 GB HBM2 SXM2 graphics card

SKU CH-HPXN-G80TC MPN V100

A data-centre GPU with 32 GB HBM2 memory and Volta architecture for AI, HPC and graphics acceleration

  • Chipset ManufacturerNVIDIA
  • GPUV100
  • Memory TypeHBM2
  • OpenGLOpenCL
  • Form FactorSXM2
  • 32 GB HBM2 memory enables large-model training
  • Volta architecture accelerates AI and HPC workloads
  • SXM2 form factor fits dense server deployments
  • Passive cooler suits rack environments with managed airflow
  • Supports OpenCL, OpenACC and DirectCompute APIs

Data-Centre AI Accelerator

The NVIDIA Tesla V100 is a graphics card built for data-centre workloads such as AI training, high-performance computing and professional graphics. It targets data scientists, researchers and engineers who need massive parallel throughput in a single module.

32 GB HBM2 Memory

The card provides 32 GB of HBM2 memory, the specification that determines whether the accelerator can hold large models and datasets without spilling to system RAM. This capacity directly governs the scale of problems the GPU can tackle.

SXM2 Passive Form Factor

The module uses an SXM2 form factor with a passive cooler, so it must be installed in a compatible server chassis that supplies forced airflow across the heatsink. It draws power and connects through the SXM2 socket, freeing PCIe slots for other expansion cards.

Points forts

  • 32 GB HBM2 memory enables large-model training
  • Volta architecture accelerates AI and HPC workloads
  • SXM2 form factor fits dense server deployments
  • Passive cooler suits rack environments with managed airflow
  • Supports OpenCL, OpenACC and DirectCompute APIs

Caractéristiques

BrandNVIDIA
SeriesTESLA V100 32GB
ModelTESLA V100 32GB
Chipset ManufacturerNVIDIA
GPUV100
Memory TypeHBM2
OpenGLOpenCL
Form FactorSXM2

Questions sur cet article

How does the 32 GB HBM2 memory translate to real-world AI training performance?

The 32 GB HBM2 capacity lets large models and datasets stay on the GPU, reducing CPU offload and speeding up AI and HPC workloads.

What does the SXM2 form factor limit in a server build?

SXM2 requires a compatible motherboard tray with the matching socket and power delivery; it cannot be used in standard PCIe slots.

What physical space and cooling does the passive SXM2 module need?

The card uses a passive cooler, so the chassis must provide directed airflow across the heatsink and enough vertical clearance for the SXM2 module height.

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