Specifications Compared
| Spec | QUADRO-RTX-5000 | V100 |
|---|---|---|
| TDP | 230W | 300W |
| VRAM | 16 GB | 16-32 GB |
| CUDA Cores | 3,072 | 5,120 |
| Memory Type | GDDR6 | HBM2 |
| Architecture | Turing | Volta |
| Form Factors | PCIe | SXM2, PCIe |
| Interconnect | NVLink | NVLink, PCIe 3.0 |
| Tensor Cores | 384 | 640 |
| FP16 Performance | 11.2 TFLOPS | 125 TFLOPS |
| FP32 Performance | 11.2 TFLOPS | 15.7 TFLOPS |
| Memory Bandwidth | 448 GB/s | 900 GB/s |
Performance Analysis
Compute performance reveals a clear leader: V100's 125 TFLOPS FP16 enables up to 11 times faster half-precision operations than RTX 5000's 11.2 TFLOPS, accelerating deep learning training and inference where models like transformers rely on FP16 for speed. V100's FP32 at 15.7 TFLOPS also exceeds RTX 5000's 11.2 TFLOPS, benefiting single-precision scientific simulations.
Memory bandwidth defines data handling: V100's 900 GB/s doubles RTX 5000's 448 GB/s, supporting larger batch sizes in training to minimize per-iteration overhead and improve throughput in memory-intensive inference. This edge proves critical for large datasets.
Power draw differs notably: RTX 5000's 230W TDP versus V100's 300W allows denser deployments in power-limited clouds, though V100's NVLink interconnect facilitates multi-GPU scaling absent in RTX 5000's PCIe focus.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
Quadro RTX 5000
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Paperspace | NVIDIA Quadro RTX 5000 16GB VRAM | 16GB | 8 vCPU 30GB RAM 50GB Storage | New York | $0.82/GPU/hr | Available | ||
Paperspace | 2×NVIDIA Quadro RTX 5000 16GB VRAM | 16GB | 16 vCPU 60GB RAM 50GB Storage | New York | $0.82/GPU/hr $1.64/hr total (2×) | Available |
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 Quadro RTX 5000
Select Quadro RTX 5000 for professional visualization and CAD workflows: its Turing RT cores optimize ray tracing absent in Volta, complemented by 16 GB GDDR6 VRAM sufficient for complex scenes. Lower 230W TDP and $0.82 per hour pricing fit single-node graphics tasks without high compute demands.
PCIe form factor simplifies integration in standard servers, ideal for hybrid viz-ML pipelines where FP32 at 11.2 TFLOPS handles moderate inference.
When to Choose the Tesla V100 32GB
Choose V100 32GB for demanding AI training and HPC: 125 TFLOPS FP16 drives efficient large-model optimization, while 900 GB/s bandwidth enables massive batches on 32 GB HBM2. NVLink and SXM2 support multi-GPU clusters for scaled throughput.
Cloud abundance at $0.29 per hour from makes it viable for bursty high-FP16 inference, outperforming RTX 5000 in tensor-heavy workloads.
Use Cases
V100's 125 TFLOPS FP16 vastly outperforms RTX 5000's 11.2 TFLOPS, enabling faster convergence on large language models. Higher 900 GB/s bandwidth supports bigger batches during training.
V100 excels with 125 TFLOPS FP16 for low-latency half-precision serving. 32 GB HBM2 handles extended context lengths better than RTX 5000's 16 GB.
V100's 15.7 TFLOPS FP32 and 125 TFLOPS FP16 accelerate parameter updates. 900 GB/s bandwidth reduces bottlenecks in dataset loading.
RTX 5000's Turing RT cores optimize diffusion denoising over V100's Volta design. 16 GB GDDR6 suffices for image generation at $0.82 per hour.
V100's 15.7 TFLOPS FP32 and 900 GB/s bandwidth excel in simulations. NVLink enables multi-GPU precision calculations.
Frequently Asked Questions
Which GPU has higher FP16 performance?▾
V100 achieves 125 TFLOPS FP16, over 11 times RTX 5000's 11.2 TFLOPS. This gap favors V100 in AI training and inference. RTX 5000 matches at 11.2 TFLOPS FP32 only.
What is the memory bandwidth difference?▾
V100 offers 900 GB/s with HBM2, doubling RTX 5000's 448 GB/s GDDR6. Higher bandwidth on V100 supports larger ML batches. Both share NVLink interconnects.
Which has more VRAM?▾
V100 32GB provides double RTX 5000's 16 GB capacity. This aids V100 in large-model hosting. Pricing starts at $0.29 per hour for V100 versus $0.82.
What are the power requirements?▾
RTX 5000 draws 230W TDP, lower than V100's 300W. Lower TDP suits dense cloud instances. V100's higher power yields better 125 TFLOPS FP16.
Which is cheaper in the cloud?▾
V100 32GB starts at $0.29 per hour averaging $1.01 across 46 offers, while RTX 5000 is $0.82 average over two. V100 offers more availability. Specs justify V100 for compute.
Which architecture is newer?▾
RTX 5000 uses 2018 Turing, succeeding V100's 2017 Volta. Turing adds RT cores for graphics. V100 retains compute lead with 125 TFLOPS FP16.
Which is cheaper to rent, the Quadro RTX 5000 or the V100?▾
Cloud rental prices for both the Quadro RTX 5000 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 Quadro RTX 5000 have compared to the V100?▾
The Quadro RTX 5000 has 16 GB of GDDR6 memory. The V100 has 16 to 32 GB of HBM2 memory.
Can I find Quadro RTX 5000 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 Quadro RTX 5000 and the V100?▾
The Quadro RTX 5000 uses the Turing architecture (2018) while the V100 uses Volta (2017). The V100 delivers 11.2x the FP16 throughput and 2.0x the memory bandwidth of the Quadro RTX 5000.