NVIDIA V100 VS NVIDIA Tesla V100S
Choosing between **V100** and **V100S** depends on your specific AI workload requirements. Currently, you can rent these GPUs starting from **$0.13/h** and **$0.88/h** respectively across 18 providers.
📊 Detailed Specifications Comparison
| Specification | V100 | V100S | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Volta | Volta | - |
| Process Node | 12nm | 12nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | SXM2 / PCIe | Dual-slot PCIe | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 32GB | 32GB | |
| Memory Type | HBM2 | HBM2 | - |
| Memory Bandwidth | 900 GB/s | 1.1 TB/s | -21% |
| Memory Bus Width | 4096-bit | 4096-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 5,120 | 5,120 | |
| Tensor Cores (AI) | 640 | N/A | |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 15.7 TFLOPS | 16.4 TFLOPS | -4% |
| FP16 (Half Precision) | 125 TFLOPS | N/A | |
| FP64 (Double Precision) | 7.8 TFLOPS | N/A | |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 300W | 250W | +20% |
| PCIe Interface | PCIe 3.0 x16 | PCIe 3.0 x16 | - |
🎯 Use Case Recommendations
LLM & Large Model Training
NVIDIA V100
Higher VRAM capacity and memory bandwidth are critical for training large language models. The V100S offers 32GB compared to 32GB.
AI Inference
NVIDIA V100
For inference workloads, performance per watt matters most. Consider the balance between FP16/INT8 throughput and power consumption.
Budget-Conscious Choice
NVIDIA V100
Based on current cloud pricing, the V100 starts at a lower hourly rate.
Technical Deep Dive: V100 vs V100S
Both GPUs utilize the NVIDIA Volta architecture. The primary difference lies in their compute core counts. From a cost perspective, the **V100** is currently about **85% cheaper** per hour, offering better value for budget-conscious projects.
NVIDIA V100 is Best For:
- Deep learning training
- Scientific research
- Latest generation workloads
NVIDIA Tesla V100S is Best For:
- HPC
- Scientific computing
- Legacy architectures
Frequently Asked Questions
Which GPU is better for AI training: V100 or V100S?
For AI training, the key factors are VRAM size, memory bandwidth, and tensor core performance. The V100 offers 32GB of HBM2 memory with 900 GB/s bandwidth, while the V100S provides 32GB of HBM2 with 1.1 TB/s bandwidth. Both GPUs have similar VRAM capacity, so performance characteristics become the deciding factor.
What is the price difference between V100 and V100S in the cloud?
Cloud GPU rental prices vary by provider and region. Based on our data, V100 starts at $0.13/hour while V100S starts at $0.88/hour. This represents a 85% price difference.
Can I use V100S instead of V100 for my workload?
It depends on your specific requirements. If your model fits within 32GB of VRAM and you don't need the additional throughput of the V100, the V100S can be a cost-effective alternative. However, for workloads requiring maximum memory capacity or multi-GPU scaling, the V100's architecture may be essential.
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