NVIDIA T4 VS NVIDIA Tesla V100S
Choosing between **T4** and **V100S** depends on your specific AI workload requirements. The **V100S** leads in both memory capacity and raw compute power, making it a stronger choice for high-end LLM training. Currently, you can rent these GPUs starting from **$0.11/h** and **$0.88/h** respectively across 11 providers.
📊 Detailed Specifications Comparison
| Specification | T4 | V100S | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Turing | Volta | - |
| Process Node | 12nm | 12nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | Single-slot PCIe | Dual-slot PCIe | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 16GB | 32GB | -50% |
| Memory Type | GDDR6 | HBM2 | - |
| Memory Bandwidth | 320 GB/s | 1.1 TB/s | -72% |
| Memory Bus Width | 256-bit | 4096-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 2,560 | 5,120 | -50% |
| Tensor Cores (AI) | 320 | N/A | |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 8.1 TFLOPS | 16.4 TFLOPS | -51% |
| FP16 (Half Precision) | 65 TFLOPS | N/A | |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 70W | 250W | -72% |
| PCIe Interface | PCIe 3.0 x16 | PCIe 3.0 x16 | - |
🎯 Use Case Recommendations
LLM & Large Model Training
NVIDIA T4
Higher VRAM capacity and memory bandwidth are critical for training large language models. The V100S offers 32GB compared to 16GB.
AI Inference
NVIDIA T4
For inference workloads, performance per watt matters most. Consider the balance between FP16/INT8 throughput and power consumption.
Budget-Conscious Choice
NVIDIA T4
Based on current cloud pricing, the T4 starts at a lower hourly rate.
Technical Deep Dive: T4 vs V100S
This is a generational comparison within the NVIDIA ecosystem, pitting Turing against Volta. The V100S has a significant **16GB VRAM advantage**, which is crucial for training massive datasets or large language models. From a cost perspective, the **T4** is currently about **88% cheaper** per hour, offering better value for budget-conscious projects.
NVIDIA T4 is Best For:
- AI inference
- Video transcoding
- Large model training
NVIDIA Tesla V100S is Best For:
- HPC
- Scientific computing
- Legacy architectures
Frequently Asked Questions
Which GPU is better for AI training: T4 or V100S?
For AI training, the key factors are VRAM size, memory bandwidth, and tensor core performance. The T4 offers 16GB of GDDR6 memory with 320 GB/s bandwidth, while the V100S provides 32GB of HBM2 with 1.1 TB/s bandwidth. For larger models, the V100S's higher VRAM capacity gives it an advantage.
What is the price difference between T4 and V100S in the cloud?
Cloud GPU rental prices vary by provider and region. Based on our data, T4 starts at $0.11/hour while V100S starts at $0.88/hour. This represents a 88% price difference.
Can I use V100S instead of T4 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 T4, the V100S can be a cost-effective alternative. However, for workloads requiring maximum memory capacity or multi-GPU scaling, the T4's architecture may be essential.
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