Specifications Compared
| Spec | TITAN-V | V100 |
|---|---|---|
| TDP | 250W | 300W |
| VRAM | 12 GB | 16-32 GB |
| CUDA Cores | 5,120 | 5,120 |
| Memory Type | HBM2 | HBM2 |
| Architecture | Volta | Volta |
| Form Factors | PCIe | SXM2, PCIe |
| Interconnect | NVLink, PCIe 3.0 | |
| Tensor Cores | 640 | 640 |
| FP16 Performance | 13.8 TFLOPS | 125 TFLOPS |
| FP32 Performance | 13.8 TFLOPS | 15.7 TFLOPS |
| FP64 Performance | 6.9 TFLOPS | 7.8 TFLOPS |
| Memory Bandwidth | 653 GB/s | 900 GB/s |
Performance Analysis
The V100 outperforms the TITAN V significantly in FP16 at 125 TFLOPS versus 13.8 TFLOPS, accelerating mixed-precision training in deep learning frameworks. This delta means training large neural networks completes up to nine times faster on the V100, as FP16 handles forward and backward passes efficiently. FP32 at 15.7 TFLOPS on the V100 edges out the TITAN V's 13.8 TFLOPS for precise inference or simulations.
Higher memory bandwidth of 900 GB/s on the V100 versus 653 GB/s supports larger batch sizes in training, reducing overhead and improving throughput for models exceeding 12 GB VRAM. The V100's 32 GB capacity handles massive datasets without swapping, unlike the TITAN V's 12 GB limit. In inference, the V100 processes more simultaneous requests due to tensor core advantages.
Power draw differs at 300W TDP for the V100 and 250W for the TITAN V, impacting density in clusters. NVLink on the V100 enables multi-GPU scaling with lower latency than PCIe-only TITAN V setups.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
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 TITAN V
The TITAN V suits desktop workstations with power constraints at 250W TDP versus the V100's 300W. Its PCIe form factor integrates easily into consumer systems without SXM2 requirements. Legacy applications needing balanced FP32 and FP16 at 13.8 TFLOPS each benefit from its 12 GB HBM2 for single-GPU tasks like prototyping.
Scenarios with no cloud needs favor the TITAN V if acquired second-hand, avoiding V100's datacenter focus.
When to Choose the Tesla V100 32GB
The V100 excels in AI training and inference requiring 125 TFLOPS FP16 and 32 GB VRAM for large models. Its 900 GB/s bandwidth supports high batch sizes, ideal for deep learning pipelines. NVLink interconnects enable multi-GPU clusters, unavailable on the TITAN V.
Cloud users access the V100 from $0.29 per hour across 46 offers, perfect for scalable workloads without hardware ownership.
Use Cases
The V100's 125 TFLOPS FP16 and 32 GB VRAM handle large language models efficiently. The TITAN V's 12 GB limits batch sizes with only 13.8 TFLOPS FP16.
V100's 900 GB/s bandwidth and tensor cores support high-throughput inference. TITAN V struggles with memory-intensive queries beyond 12 GB.
32 GB VRAM on V100 accommodates fine-tuning datasets; 15.7 TFLOPS FP32 aids precision. TITAN V's lower specs slow iterations.
V100's high FP16 and bandwidth generate images faster at scale. TITAN V's 12 GB VRAM restricts resolution and batch sizes.
TITAN V's 13.8 TFLOPS FP32 suffices for single-node simulations at 250W. V100's NVLink scales complex HPC jobs.
Frequently Asked Questions
What is the VRAM capacity of TITAN V versus V100 32GB?▾
The TITAN V provides 12 GB HBM2 VRAM. The V100 offers 32 GB HBM2, enabling larger models. This difference impacts batch sizes in training.
How do FP16 performance levels compare?▾
TITAN V achieves 13.8 TFLOPS FP16. V100 reaches 125 TFLOPS FP16 with tensor cores. The gap accelerates deep learning by up to ninefold.
What are the memory bandwidth specs?▾
TITAN V has 653 GB/s bandwidth. V100 delivers 900 GB/s. Higher bandwidth on V100 supports bigger batches without slowdowns.
What is the power consumption difference?▾
TITAN V uses 250W TDP. V100 requires 300W TDP. Lower power aids TITAN V in desktops.
Is cloud pricing available for these GPUs?▾
TITAN V has no live offers. V100 32GB starts at $0.29 per hour, averaging $1.01 per hour across 46 providers.
What interconnects do they support?▾
TITAN V uses PCIe only. V100 supports NVLink and PCIe 3.0 for multi-GPU scaling.
Which is cheaper to rent, the TITAN V or the V100?▾
Cloud rental prices for both the TITAN V 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 TITAN V have compared to the V100?▾
The TITAN V has 12 GB of HBM2 memory. The V100 has 16 to 32 GB of HBM2 memory.
Can I find TITAN V 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 TITAN V and the V100?▾
The TITAN V uses the Volta architecture (2017) while the V100 uses Volta (2017). The V100 delivers 9.1x the FP16 throughput and 1.4x the memory bandwidth of the TITAN V.