Interconnect: NVLink 2
Software Stack: CUDA, cuDNN, TensorRT, NVIDIA AI Enterprise
Organizations that historically deployed V100 GPUs include:
Although newer GPUs are now preferred for large training workloads, the V100 remains relevant in environments where cost-effective GPU compute is sufficient.
The V100 is still available in many secondary hardware markets and cloud GPU platforms. Because it has been replaced by newer architectures, it is often available at lower cost compared with modern AI accelerators.
Many organizations continue to use V100 GPUs in research clusters and legacy AI infrastructure.
The NVIDIA V100 is a high-performance data center GPU built on the Volta architecture, designed to accelerate deep learning training and high-performance computing workloads. At the time of its release, the V100 represented a major advancement in GPU architecture for artificial intelligence applications.
The GPU introduced first-generation Tensor Cores, which significantly improved performance for deep learning operations such as matrix multiplications used in neural network training. Combined with high-bandwidth HBM2 memory, the V100 enabled efficient training of increasingly large deep learning models.
The architecture was widely adopted across research institutions, cloud platforms, and enterprise AI infrastructure. As a result, the V100 became one of the most influential GPUs in the development of modern machine learning frameworks and large-scale AI training systems.
| Specification | Details |
|---|---|
| GPU | V100 |
| Architecture | NVIDIA Volta |
| CUDA Cores | 5,120 |
| Tensor Cores | 640 (1st Gen) |
| Memory | 16 GB or 32 GB HBM2 |
| Memory Bandwidth | ~900 GB/s |
| Interconnect | NVLink 2 |
| Form Factor | SXM / PCIe |
| Max TGP | ~300 W |
| Precision Support | FP64, FP32, FP16 |
| Typical AI Compute | ~125 TFLOPS (FP16 Tensor) |
| Process Node | TSMC 12 nm |
| Transistor Count | ~21 billion |
| MIG Support | Not supported |
| NVLink (peer) | ~300 GB/s bidirectional |
Although newer GPUs such as the A100 and H100 offer much higher performance, the V100 remains an important reference point in the evolution of AI hardware.
The GPU played a major role in enabling early large-scale training of transformer-based neural networks.
Comparable NVIDIA GPUs:
Competing Accelerators:
These newer platforms provide improved compute throughput and memory capacity for modern AI workloads.
Related NVIDIA GPUs:
Complementary Infrastructure:
The NVIDIA V100 is a Volta-based data center GPU that played a pivotal role in the advancement of deep learning and high-performance computing. By introducing Tensor Core acceleration and high-bandwidth HBM2 memory, the architecture enabled significant improvements in AI training performance.
Although newer GPUs now dominate modern AI infrastructure, the V100 remains an important milestone in the evolution of AI hardware and continues to be used in research and legacy compute environments.
