NVIDIA V100
Proven Volta-generation GPU for inference and legacy AI workloads
VRAM
32 GB
Bandwidth
900 GB/s
FP16
125 TFLOPS
TDP
300W (SXM2) / 250W (PCIe)
Technical Specifications
| VRAM | 32 GB HBM2 |
| Memory Bandwidth | 900 GB/s |
| FP16 Performance | 125 TFLOPS |
| FP32 Performance | 15.7 TFLOPS |
| INT8 Performance | 62 TOPS |
| TDP | 300W (SXM2) / 250W (PCIe) |
| Form Factor | SXM2 / PCIe Gen3 |
| Interconnect | NVLink 2.0 (300 GB/s, SXM2 only) |
| PCIe Interface | PCIe Gen3 x16 |
| Max GPUs per Server | 8 (DGX-1V / HGX V100) / 4 (PCIe) |
Prices vary with supply and import costs. Contact for current India pricing.
Best For
Not Ideal For
- Training large LLMs (newer GPUs offer 5-8x more throughput)
- Workloads needing BF16 support (V100 only supports FP16)
- New deployments where power efficiency matters (older architecture)
Overview
The NVIDIA V100 was the first Tensor Core GPU and defined the modern era of deep learning acceleration. Built on the Volta architecture with 32 GB of HBM2 memory, it remains a capable GPU for inference and smaller-scale training workloads at a fraction of the cost of newer-generation hardware.
For inference, the V100 handles most production models including BERT, ResNet, Whisper, and LLaMA 7B with room to spare. Its 32 GB VRAM is sufficient for the majority of inference use cases. For organizations running proven models in production, the V100 offers excellent cost efficiency.
The V100 is widely available in the refurbished market, making it an attractive option for academic institutions, startups, and research labs that need GPU compute without the premium of current-generation hardware. We supply certified refurbished V100 SXM2 and PCIe units with warranty.
Note: The V100 does not support BF16 data type. If your framework requires BF16, consider the A100 or newer. For FP16 and FP32 workloads, the V100 remains fully supported by all modern deep learning frameworks.
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