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
The V100 dominates in compute-heavy workloads due to its 125 TFLOPS FP16 performance compared to 11.2 TFLOPS on the Quadro RTX 5000: this delta accelerates mixed-precision training by up to 11 times, vital for large models where FP16 reduces memory footprint without precision loss. FP32 rates show the V100 at 15.7 TFLOPS versus 11.2 TFLOPS, a 40 percent edge for single-precision inference or simulations. Memory bandwidth tells another story: 900 GB/s on the V100 HBM2 versus 448 GB/s GDDR6 on the Quadro RTX 5000 enables larger batch sizes in training, minimizing data starvation in deep learning pipelines. The Quadro RTX 5000's equal FP16 and FP32 at 11.2 TFLOPS suits graphics tasks, but its 230W TDP trails the V100's 300W, implying lower density in multi-GPU setups. Overall, bandwidth and FP16 superiority position the V100 for throughput-oriented AI.
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 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 Quadro RTX 5000
Opt for the Quadro RTX 5000 in graphics-intensive applications like CAD rendering or real-time visualization: its Turing architecture includes RT cores absent in Volta, enhancing ray tracing at 11.2 TFLOPS FP32. Lower 230W TDP suits power-constrained clouds, and $0.82 per hour pricing aligns with sporadic professional workflows across two offers.
When to Choose the Tesla V100 16GB
Select the V100 16GB for machine learning training and HPC: 125 TFLOPS FP16 crushes mixed-precision tasks, while 900 GB/s bandwidth supports massive datasets. Availability from $0.10 per hour across 25 offers makes it economical for sustained AI compute.
Use Cases
V100's 125 TFLOPS FP16 vastly outpaces Quadro RTX 5000's 11.2 TFLOPS, enabling faster mixed-precision training for large language models. Higher 900 GB/s bandwidth handles massive batches efficiently.
V100 delivers 125 TFLOPS FP16 for high-throughput inference, superior to 11.2 TFLOPS on Quadro RTX 5000. 16 GB HBM2 at 900 GB/s supports larger models without bottlenecks.
Volta's 15.7 TFLOPS FP32 and 125 TFLOPS FP16 excel in fine-tuning precision tasks over Turing's balanced 11.2 TFLOPS rates.
Quadro RTX 5000's Turing RT cores optimize diffusion model rendering, paired with 11.2 TFLOPS FP32 for graphics workloads.
V100's 900 GB/s HBM2 bandwidth and 15.7 TFLOPS FP32 accelerate simulations far beyond Quadro RTX 5000's 448 GB/s and 11.2 TFLOPS.
Frequently Asked Questions
Which GPU has higher FP16 performance?▾
The V100 achieves 125 TFLOPS FP16, dwarfing the Quadro RTX 5000's 11.2 TFLOPS. This makes V100 ideal for half-precision AI training. Bandwidth also favors V100 at 900 GB/s over 448 GB/s.
What is the price difference?▾
V100 16GB starts at $0.10 per hour with an average of $0.81 per hour across 25 offers. Quadro RTX 5000 is $0.82 per hour average across two offers. V100 offers better value for compute-heavy tasks.
Does VRAM differ between them?▾
Both provide 16 GB, but V100 uses HBM2 while Quadro RTX 5000 has GDDR6. V100's 900 GB/s bandwidth outperforms 448 GB/s for data-intensive workloads.
Which has lower power consumption?▾
Quadro RTX 5000 draws 230W TDP versus V100's 300W. This suits power-sensitive deployments, though V100 delivers more performance per watt in FP16.
Are they compatible with NVLink?▾
Both support NVLink for multi-GPU scaling. V100 also includes PCIe 3.0, enhancing datacenter flexibility.
Which is newer?▾
Quadro RTX 5000 uses 2018 Turing architecture, post-2017 Volta in V100. Despite age, V100's specs remain superior for ML.
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.