Specifications Compared
| Spec | QUADRO-RTX-5000 | RTX-2070 |
|---|---|---|
| TDP | 230W | 175W |
| VRAM | 16 GB | 8 GB |
| CUDA Cores | 3,072 | 2,304 |
| Memory Type | GDDR6 | GDDR6 |
| Architecture | Turing | Turing |
| Form Factors | PCIe | PCIe |
| Interconnect | NVLink | NVLink |
| Tensor Cores | 384 | 288 |
| FP16 Performance | 11.2 TFLOPS | 7.5 TFLOPS |
| FP32 Performance | 11.2 TFLOPS | 7.5 TFLOPS |
| Memory Bandwidth | 448 GB/s | 448 GB/s |
Performance Analysis
The Quadro RTX 5000 outperforms the RTX 2070 SUPER in raw compute with 11.2 TFLOPS FP16 and FP32 versus 7.5 TFLOPS, translating to approximately 49 percent higher throughput for training and inference in mixed-precision workflows. This FP16 and FP32 parity on both GPUs supports efficient tensor core utilization without precision bottlenecks. In real-world terms, the Quadro handles larger batch sizes during LLM training due to its 16 GB VRAM doubling the RTX 2070 SUPER's 8 GB, reducing out-of-memory errors for models exceeding 8 GB. Memory bandwidth matches at 448 GB/s, so data transfer rates remain equivalent, but the Quadro's extra VRAM enables sustained performance on high-resolution inference or fine-tuning without swapping. The Quadro's 230 W TDP versus 175 W reflects its capacity for prolonged heavy loads, while the RTX 2070 SUPER suits shorter, lighter sessions.
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 |
When to Choose the Quadro RTX 5000
Select the Quadro RTX 5000 for workloads demanding over 8 GB VRAM, such as training large language models or Stable Diffusion with high-resolution outputs. Its 16 GB capacity and 11.2 TFLOPS performance excel in professional visualization and scientific simulations requiring NVLink multi-GPU scaling. Cloud availability at $0.82 per hour makes it practical for sustained compute sessions.
When to Choose the RTX 2070 SUPER
Choose the RTX 2070 SUPER for cost-sensitive, power-constrained environments like edge inference or gaming-integrated ML prototypes. Its 175 W TDP lowers operational costs compared to the Quadro's 230 W, and 7.5 TFLOPS suffices for batch sizes fitting within 8 GB VRAM. Absence of live cloud offers suggests on-premises preference for lighter tasks.
Use Cases
The Quadro RTX 5000's 16 GB VRAM supports larger models and batch sizes critical for LLM training, unlike the RTX 2070 SUPER's 8 GB limit. Its 11.2 TFLOPS FP16 outperforms the 7.5 TFLOPS for faster convergence.
Quadro RTX 5000 delivers 11.2 TFLOPS FP32 for quicker inference on memory-heavy LLMs, with 16 GB VRAM accommodating bigger contexts. RTX 2070 SUPER's 8 GB restricts deployment scale.
16 GB VRAM on Quadro RTX 5000 prevents memory errors during fine-tuning of mid-sized models, paired with 11.2 TFLOPS for efficient iterations. RTX 2070 SUPER's 8 GB suits only smaller adapters.
Quadro RTX 5000's doubled 16 GB VRAM enables high-resolution image generation without tiling, boosted by 11.2 TFLOPS. RTX 2070 SUPER's 8 GB limits output quality.
Both offer 448 GB/s bandwidth and NVLink for simulations, but Quadro RTX 5000's 16 GB VRAM aids complex datasets while RTX 2070 SUPER's 175 W TDP fits low-power clusters.
Frequently Asked Questions
Which GPU has more VRAM: Quadro RTX 5000 or RTX 2070 SUPER?▾
The Quadro RTX 5000 provides 16 GB GDDR6 VRAM, double the RTX 2070 SUPER's 8 GB. This difference impacts handling of large ML models. Both share 448 GB/s bandwidth.
What are the FP32 performance differences?▾
Quadro RTX 5000 achieves 11.2 TFLOPS FP32, surpassing the RTX 2070 SUPER's 7.5 TFLOPS by 49 percent. This boosts training and inference speeds. FP16 matches these figures on each.
How do power consumptions compare?▾
Quadro RTX 5000 draws 230 W TDP, higher than RTX 2070 SUPER's 175 W. Lower TDP on SUPER reduces cooling needs. Both use PCIe form factor.
Is cloud pricing available for these GPUs?▾
Quadro RTX 5000 offers start from $0.82 per hour across two providers, averaging $0.82 per hour. RTX 2070 SUPER has no live cloud offers. Check gpuperhour.com for updates.
Do both support NVLink?▾
Yes, both Quadro RTX 5000 and RTX 2070 SUPER include NVLink interconnect for multi-GPU setups. This aids scaling in compute clusters. They share Turing architecture from 2018.
Which is better for machine learning batch sizes?▾
Quadro RTX 5000's 16 GB VRAM supports larger batches than RTX 2070 SUPER's 8 GB. Matching 448 GB/s bandwidth ensures no transfer bottlenecks. Higher 11.2 TFLOPS accelerates processing.
Which is cheaper to rent, the Quadro RTX 5000 or the RTX 2070?▾
Cloud rental prices for both the Quadro RTX 5000 and RTX 2070 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 RTX 2070?▾
The Quadro RTX 5000 has 16 GB of GDDR6 memory. The RTX 2070 has 8 GB of GDDR6 memory.
Can I find Quadro RTX 5000 and RTX 2070 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 RTX 2070?▾
The Quadro RTX 5000 uses the Turing architecture (2018) while the RTX 2070 uses Turing (2018). The Quadro RTX 5000 delivers 1.5x the FP16 throughput and 1.0x the memory bandwidth of the RTX 2070.