V100 32GB PCIe

Specs, explained

Used for HPC and AI training

1. 0132 GB of HBM2 memory

   VRAM

   32 GB

   Memory type

   HBM2

   Memory bandwidth

   0.9 TB/s

   HBM (High Bandwidth Memory) is stacked in layers and sits right next to the processor on the same package. That short, wide path is what gives accelerators with HBM their memory bandwidth, so they are usually chosen for work limited by how fast data reaches the cores, such as large-model inference and HPC simulation.

   VRAM is the working space on the card. Model weights, activations and datasets have to fit in it, or be split across several GPUs. Rule of thumb for the weights alone at 16-bit precision (about 2 GB per billion parameters): up to roughly 16 billion parameters, before activations and cache. This is arithmetic on the recorded VRAM, not a benchmark.

2. 02Volta architecture

   Architecture

   Volta

   Launch year

   2017

   Lifecycle status

   Legacy

   Volta introduced Tensor Cores, dedicated matrix-math units for deep learning. (General context about the architecture, not a statement about this specific card.)

   Launched in 2017. Kovara's records list it as a legacy part.

3. 03Form factor and power

   NVLink

   Not supported

   MIG

   Not supported

   Form factor

   PCIe dual-slot FHHL

   TDP

   250 W

   Kovara's records show no NVLink support, so multi-GPU jobs on this part rely on PCIe or the network between servers to move data.

   PCIe cards slot into standard server and workstation expansion slots, which suits a wide range of systems.

   TDP is the rated thermal design power: the planning figure for power delivery and cooling in a server.

4. 04What it is used for

   Primary use (recorded)

   HPC / AI training

   These are the uses Kovara has recorded for this part. The notes explain the workload in general terms; they are not performance claims.

   • HPCScientific and engineering simulation, where numerical precision and memory bandwidth matter.
   • AI trainingTeaching a model from data. Usually the most compute- and memory-hungry phase, often spread across many GPUs.

Figures are Kovara master data, last verified 03 Sept 2026. Explanations describe the technology in general; they are not benchmarks and say nothing about price or availability.