Tesla P100 16GB SXM2

Specs, explained

Used for HPC and legacy AI training

1. 0116 GB of HBM2 memory

   VRAM

   16 GB

   Memory type

   HBM2

   Memory bandwidth

   0.732 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 8 billion parameters, before activations and cache. This is arithmetic on the recorded VRAM, not a benchmark.

2. 02Pascal architecture

   Architecture

   Pascal

   Launch year

   2016

   Lifecycle status

   Legacy

   Pascal introduced NVLink to NVIDIA's data-center GPUs, first on the Tesla P100. (General context about the architecture, not a statement about this specific card.)

   Launched in 2016. Kovara's records list it as a legacy part.

3. 03Built to scale across GPUs

   NVLink

   Supported

   MIG

   Not supported

   Form factor

   SXM2

   TDP

   300 W

   NVLink is a direct GPU-to-GPU link. Where it is supported, GPUs in the same server can exchange data faster than over PCIe alone, which matters when one training job is spread across several GPUs.

   SXM modules are mounted directly on a server baseboard rather than slotted in as cards, the layout typically used for dense multi-GPU servers.

   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 / legacy 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.
   • legacy 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.