Specifications Compared
| Spec | T4 | V100 |
|---|---|---|
| TDP | 70W | 300W |
| VRAM | 16 GB | 16-32 GB |
| CUDA Cores | 2,560 | 5,120 |
| Memory Type | GDDR6 | HBM2 |
| Architecture | Turing | Volta |
| Form Factors | PCIe | SXM2, PCIe |
| Interconnect | NVLink, PCIe 3.0 | |
| Tensor Cores | 320 | 640 |
| FP16 Performance | 8.1 TFLOPS | 125 TFLOPS |
| FP32 Performance | 8.1 TFLOPS | 15.7 TFLOPS |
| INT8 Performance | 130 TOPS | |
| Memory Bandwidth | 320 GB/s | 900 GB/s |
Performance Analysis
Compute performance differs markedly: V100 achieves 125 TFLOPS FP16 versus T4's 8.1 TFLOPS, accelerating mixed-precision training for large models. FP32 rates show V100 at 15.7 TFLOPS against T4's 8.1 TFLOPS, aiding precise simulations.
Memory bandwidth impacts workloads directly: V100's 900 GB/s supports larger batch sizes in training compared to T4's 320 GB/s, reducing bottlenecks in data-heavy tasks. T4's equal FP16 and FP32 performance suits inference where low latency matters over peak throughput.
Power draw influences deployment: T4's 70W TDP enables dense server packing, while V100's 300W demands robust cooling but delivers superior raw speed.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
Tesla T4
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 AWS | NVIDIA Tesla T4 16GB VRAM | 16GB | 4 vCPU 16GB RAM | Virginia | $0.53/GPU/hr | |||
AWS | NVIDIA Tesla T4 16GB VRAM | 16GB | 8 vCPU 32GB RAM | Virginia | $0.75/GPU/hr | |||
AWS | 4×NVIDIA Tesla T4 16GB VRAM | 16GB | 48 vCPU 192GB RAM | Virginia | $0.98/GPU/hr $3.91/hr total (4×) | |||
AWS | NVIDIA Tesla T4 16GB VRAM | 16GB | 16 vCPU 64GB RAM | Virginia | $1.20/GPU/hr | |||
AWS | NVIDIA Tesla T4 16GB VRAM | 16GB | 32 vCPU 128GB RAM | Virginia | $2.18/GPU/hr |
Tesla V100 32GB
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 TensorDock | NVIDIA Tesla V100 16GB 16GB VRAM | 16GB | 0 vCPU 0GB RAM | Texas | $0.19/GPU/hr | Available | ||
TensorDock | NVIDIA Tesla V100 16GB 16GB VRAM | 16GB | 0 vCPU 0GB RAM | New York City | $0.19/GPU/hr | Available | ||
TensorDock | NVIDIA Tesla V100 32GB 32GB VRAM | 32GB | 0 vCPU 0GB RAM | Texas | $0.29/GPU/hr | Available | ||
TensorDock | NVIDIA Tesla V100 32GB 32GB VRAM | 32GB | 0 vCPU 0GB RAM | New York City | $0.29/GPU/hr | Available | ||
 Lambda Labs | 8×NVIDIA Tesla V100 16GB 16GB VRAM | 16GB | 88 vCPU 448GB RAM 6041GB Storage | Texas | $0.79/GPU/hr $6.32/hr total (8×) | Available |
When to Choose the Tesla T4
Select the T4 for inference-dominated applications. Its 70W TDP and $0.53/hr starting price minimize operational costs in high-density cloud instances. The PCIe form factor and 320 GB/s bandwidth handle real-time serving efficiently.
T4 fits budget-conscious setups prioritizing energy efficiency over peak compute.
When to Choose the Tesla V100 32GB
Choose V100 32GB for training and fine-tuning intensive tasks. The 125 TFLOPS FP16 and 900 GB/s bandwidth enable faster iterations on large datasets. NVLink interconnect enhances multi-GPU scaling.
At $0.29/hr starting price, it offers high performance across 46 cloud offers.
Use Cases
V100's 125 TFLOPS FP16 provides over 15 times the performance of T4's 8.1 TFLOPS for mixed-precision training. Its 32 GB VRAM handles massive models.
T4's 70W TDP and balanced 8.1 TFLOPS FP16/FP32 enable efficient, low-cost serving. It suits high-volume inference without V100's 300W power needs.
V100's 900 GB/s bandwidth and 32 GB HBM2 VRAM support larger batches than T4's 320 GB/s and 16 GB. This speeds up iterative fine-tuning.
T4 suffices for inference with low power; V100 excels in training via 125 TFLOPS FP16. Choice depends on workload emphasis.
V100's 15.7 TFLOPS FP32 outperforms T4's 8.1 TFLOPS for simulations. Higher bandwidth aids complex computations.
Frequently Asked Questions
Is the T4 or V100 better for ML training?▾
The V100 excels with 125 TFLOPS FP16 versus T4's 8.1 TFLOPS, enabling faster training. Its 32 GB VRAM and 900 GB/s bandwidth handle large batches better. T4 suits lighter tasks at 70W TDP.
Which has higher memory bandwidth, T4 or V100?▾
V100 offers 900 GB/s HBM2 bandwidth compared to T4's 320 GB/s GDDR6. This allows V100 larger batch sizes in training. T4 remains adequate for inference.
T4 vs V100 power consumption?▾
T4 consumes 70W TDP, far lower than V100's 300W. This makes T4 ideal for dense deployments. V100 justifies higher power with superior 15.7 TFLOPS FP32.
Cloud pricing for T4 and V100?▾
T4 starts at $0.53/hr average $1.66/hr across 6 offers; V100 32GB at $0.29/hr average $1.01/hr across 46 offers. V100 provides better value for performance.
Does V100 have more VRAM than T4?▾
V100 32GB has double the 16 GB GDDR6 of T4. HBM2 type enhances V100 bandwidth to 900 GB/s. Both suffice for mid-size models.
T4 or V100 for inference?▾
T4 is preferable with 8.1 TFLOPS FP32/FP16 and 70W TDP for cost-effective serving. V100's higher specs suit mixed workloads. Pricing favors V100 slightly at $0.29/hr start.
Which is cheaper to rent, the T4 or the V100?▾
Cloud rental prices for both the T4 and V100 vary by provider, configuration, and availability. This page shows live pricing from 25+ providers updated every 60 seconds. Scroll to the Live Cloud Pricing section to compare current rates.
How much VRAM does the T4 have compared to the V100?▾
The T4 has 16 GB of GDDR6 memory. The V100 has 16 to 32 GB of HBM2 memory.
Can I find T4 and V100 GPUs available to rent right now?▾
Yes. This page shows real-time availability across 25+ cloud GPU providers. The Live Cloud Pricing section displays only in-stock offers with current pricing.
What is the main difference between the T4 and the V100?▾
The T4 uses the Turing architecture (2018) while the V100 uses Volta (2017). The V100 delivers 15.4x the FP16 throughput and 2.8x the memory bandwidth of the T4.