NVIDIA TESLA P100 16 GB
HPC and deep learning.
GPU accelerator based on the Pascal architecture with HBM2 memory.
3,584 CUDA cores16 GB HBM2732 GB/s18.7 TFLOPS FP16
Deep learning
Training neural
networks
16 GB HBM2
High-speed memory
with high bandwidth
FP64 computing
4.7 TFLOPS
for HPC and simulations
Full specification
- Architecture
- NVIDIA Pascal (GP100)
- Memory
- 16 GB HBM2
- Memory bandwidth
- 732 GB/s
- CUDA cores
- 3,584
- FP64 performance
- 4.7 TFLOPS
- FP32 performance
- 9.3 TFLOPS
- FP16 performance
- 18.7 TFLOPS
- Interface
- PCIe 3.0 ×16
- TDP
- 250 W
- Cooling
- Passive (server) cooling
- Form factor
- Dual-slot, full-height
- Power connection
- 8-pin EPS
- In the box
- 2× PCIe 8-pin → EPS 8-pin power cable
- Condition
- Used
- Warranty
- 3 months
- Qualification
- 6-stage technical qualification (including 600 s of gpu-burn testing) — PASS result, qualification certificate
Knowledge baseGuides and tests for this model6 articles
GPUs for a laboratory or university without an H100 budget - how to build an AI/HPC workstation for less
Tesla P40 vs V100 for a local LLM/RAG - 24 GB of VRAM or the faster Volta?
Why the prompt is slow on a P40 - and how to really improve it
2× GPU for LLMs: how llama.cpp splits a model between P40 and V100
PLLuM locally on a used Tesla - how to run it and how much VRAM
Benchmarks: Bielik and PLLuM on the P40/P100/V100 - real tokens/s with prefill time
