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
The V100 dominates in FP16 performance at 125 TFLOPS compared to T4's 8.1 TFLOPS: this gap accelerates neural network training by over 15 times in mixed-precision workflows. FP32 throughput further favors V100 at 15.7 TFLOPS over T4's 8.1 TFLOPS, benefiting single-precision scientific simulations. For inference, T4's equal FP16 and FP32 rates enable consistent throughput in deployment scenarios. Memory bandwidth presents a stark contrast: V100's 900 GB/s handles larger batch sizes and complex models without bottlenecks, unlike T4's 320 GB/s which limits scalability in data-heavy tasks. T4's 70W TDP supports higher density deployments, reducing cooling costs versus V100's 300W draw. Overall, V100 excels in compute-bound training, while T4 prioritizes power-efficient inference.
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 16GB
| 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
Choose the T4 for inference-heavy workloads requiring low power consumption. Its 70W TDP enables denser server configurations compared to V100's 300W, ideal for edge or multi-GPU inference setups. At PCIe form factor exclusivity, T4 fits standard cloud instances where 8.1 TFLOPS FP32 suffices for real-time serving, especially with cloud pricing from $0.53 per hour.
When to Choose the Tesla V100 16GB
Select the V100 for training and high-throughput compute tasks. The 125 TFLOPS FP16 crushes T4's 8.1 TFLOPS, slashing training times for deep learning models. Superior 900 GB/s bandwidth supports massive datasets, and NVLink interconnect boosts multi-GPU scaling, all at lower average cloud cost of $0.82 per hour.
Use Cases
V100's 125 TFLOPS FP16 provides over 15 times the throughput of T4's 8.1 TFLOPS, drastically reducing training times for large language models.
T4's balanced 8.1 TFLOPS FP16 and FP32 at 70W TDP excels in efficient, high-density inference serving. Lower power suits sustained deployment over V100's 300W.
V100's 15.7 TFLOPS FP32 and 900 GB/s bandwidth handle fine-tuning datasets better than T4's 8.1 TFLOPS and 320 GB/s.
V100's superior 125 TFLOPS FP16 accelerates diffusion model generation far beyond T4's 8.1 TFLOPS.
V100's 15.7 TFLOPS FP32 and NVLink support outperform T4's 8.1 TFLOPS for parallel simulations.
Frequently Asked Questions
Which GPU has higher FP16 performance, T4 or V100?▾
The V100 achieves 125 TFLOPS FP16, over 15 times higher than T4's 8.1 TFLOPS. This makes V100 ideal for training. T4 balances FP16 with FP32 at 8.1 TFLOPS for inference.
What is the memory bandwidth difference between T4 and V100?▾
V100 offers 900 GB/s with HBM2, nearly three times T4's 320 GB/s GDDR6. Higher bandwidth on V100 supports larger batches. T4 suffices for lighter workloads.
How do cloud prices compare for T4 vs V100 16GB?▾
V100 starts at $0.10 per hour (average $0.82 across 27 offers), cheaper than T4's $0.53 (average $1.66 across 6 offers). More V100 availability drives lower costs. Prices fluctuate by provider.
Which has lower power consumption?▾
T4 uses 70W TDP, far below V100's 300W. This allows more T4 GPUs per server. V100 demands robust cooling for its power.
Is V100 or T4 better for multi-GPU setups?▾
V100 supports NVLink and SXM2 for faster interconnects over T4's PCIe-only. This boosts scaling in training clusters. T4 fits single-GPU inference.
Do T4 and V100 have the same VRAM?▾
Both provide 16 GB, but V100 uses HBM2 and T4 GDDR6. V100's type pairs with 900 GB/s bandwidth for better performance. Capacity matches for mid-size models.
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.