Let us discuss NVIDIA Tesla V100 Price, Architecture, advantages, disadvantages, and Specifications in this article.
NVIDIA Tesla V100
The data center GPU NVIDIA Tesla V100 is optimized for deep learning, AI training and inference, scientific simulation, and HPC. With its Volta architecture and Tensor Cores, it raised the bar for parallel processing speed when it was released in 2017.
Architecture
- Volta is the GPU architecture.
- Manufacturing Node: TSMC’s 12nm FFN.
- There are 21.1 billion transistors.
- 5120 CUDA cores.
- 640 Tensor Cores (made for tasks including deep learning).
- Multiprocessors (SMs) for streaming: 80.
- Form Factor: SXM2 and PCIe choices.
The V100 significantly increased the performance of AI workloads by introducing Tensor Cores for the first time. Additionally, Volta has a unified memory architecture that enhanced performance by enabling data sharing across CPU and GPU memory areas.
Gaming Performance
- Not made with gameplay in mind: No DisplayPort or HDMI outputs.
- Driver support is tailored for workloads in computation and data science.
- No features for game optimisation or GeForce Experience.
Since this is a data center card rather than a consumer GPU, gaming is not advised.
Features
- Support for NVLink for fast GPU-GPU interconnects is included in the SXM2 version.
- Tensor Core operations: AI acceleration using FP16 matrix math.
- ECC memory for data integrity in contexts with a lot of computation.
- Support for multi-tenant cloud setups with large-scale virtualization.
- Support for AI training frameworks includes CUDA, cuDNN, and NCCL.
Outstanding for scientific computation, AI, and HPC.
AI and Compute Performance
Workloads using deep learning and machine learning were intended for the V100:
- Tensor performance up to 125 TFLOPS (with mixed precision).
- Outstanding results for scientific simulations in FP64 (7.8 TFLOPS).
- TensorFlow, PyTorch, MXNet, and more frameworks are supported.
At its height, this made it the preferred GPU for training big models like BERT, ResNet, and GPT-2.
Some older data centres and inference applications continue to utilise it.
Memory and Bandwidth
| Model | VRAM | Memory Type | Bus Width | Bandwidth |
|---|---|---|---|---|
| V100 PCIe | 16 GB | HBM2 | 4096-bit | 900 GB/s |
| V100 SXM2 | 32 GB | HBM2 | 4096-bit | 1134 GB/s |
The V100 could manage very big datasets without experiencing memory problems with its incredibly fast HBM2 memory. For large AI models, the 32 GB version is best.
Outstanding memory performance for models with a huge scale.
Power and Efficiency
| Form Factor | TDP |
|---|---|
| PCIe | 250W |
| SXM2 | 300W |
The V100 has significantly higher computing throughput per watt than its predecessors, but using more power than standard GPUs. For data centers to accommodate this GPU, effective cooling methods are required. Not appropriate for PC settings, yet effective for data centers.
Advantages
- Tensor and CUDA cores provide enormous acceleration for AI and HPC.
- High bandwidth and huge capacity are features of HBM2 memory.
- GPU clustering in data centres is supported by NVLink.
- Data reliability is guaranteed by ECC memory.
- Many cloud AI providers, including AWS and Google Cloud, are still supported.
Disadvantages
- Not appropriate for everyday PC use or gaming.
- Expensive (launched at between $8,000 to $10,000).
- requires specialised infrastructure and is power-hungry.
- Recently released GPUs (A100, H100, and L40) have partially replaced it.
NVIDIA Tesla V100 Price
| Model | Form Factor | VRAM | Approx. Price (USD) |
|---|---|---|---|
| Tesla V100 16GB | PCIe | 16 GB | $2,000 – $3,000 |
| Tesla V100 32GB | SXM2 | 32 GB | $3,500 – $5,000 |
| Tesla V100 32GB | PCIe | 32 GB | $3,000 – $4,500 |
NVIDIA Tesla V100 Specifications
| Specification | Tesla V100 (PCIe) | Tesla V100 (SXM2) |
|---|---|---|
| Architecture | NVIDIA Volta | NVIDIA Volta |
| GPU | GV100 | GV100 |
| Process Node | 12nm FFN (TSMC) | 12nm FFN (TSMC) |
| CUDA Cores | 5,120 | 5,120 |
| Tensor Cores | 640 | 640 |
| Base Clock | 1,235 MHz | 1,295 MHz |
| Boost Clock | 1,380 MHz | 1,530 MHz |
| Memory | 16 GB / 32 GB HBM2 | 16 GB / 32 GB HBM2 |
| Memory Bandwidth | 900 GB/s | 900 GB/s |
| Memory Interface | 4,096-bit | 4,096-bit |
| FP32 Performance | ~14 TFLOPS | ~15.7 TFLOPS |
| Tensor Performance | ~112 TFLOPS | ~125 TFLOPS |
| TDP (Power Consumption) | 250W | 300W |
| Interface | PCIe 3.0 | NVLink |
| Form Factor | Dual-slot PCIe | SXM2 Module |
| ECC Memory Support | Yes | Yes |
| Target Use | AI/ML, HPC, Data Center | AI/ML, HPC, Data Center |
Conclusion
In 2017, the NVIDIA Tesla V100, a ground-breaking GPU for AI applications in data centers and enterprises, provided compute and memory performance never before seen. Even now, in some applications, it is still a potent engine for AI training, scientific modelling, and HPC, despite not being designed for gaming or consumer-grade activities.
Select the Tesla V100 in the event that you are working on:
- Training AI models on a large scale.
- Simulations used in science.
- Inferencing at the enterprise level.
- Applications for cloud-based HPC.
Stay away from it because
- Workstations for artistic or gaming purposes.
- All-purpose desktop computers.
You can also read NVIDIA GeForce RTX 30 Series vs 20 Series Price And Specs
