Specifications Compared
| Spec | QUADRO-RTX-4000 | RTX-2080 |
|---|---|---|
| TDP | 160W | 215W |
| VRAM | 8 GB | 8-11 GB |
| CUDA Cores | 2,304 | 2,944 |
| Memory Type | GDDR6 | GDDR6 |
| Architecture | Turing | Turing |
| Form Factors | PCIe | PCIe |
| Interconnect | NVLink | |
| Tensor Cores | 288 | 368 |
| FP16 Performance | 7.1 TFLOPS | 10.1 TFLOPS |
| FP32 Performance | 7.1 TFLOPS | 10.1 TFLOPS |
| Memory Bandwidth | 416 GB/s | 616 GB/s |
Performance Analysis
The RTX 2080 provides 42 percent higher FP32 performance at 10.1 TFLOPS versus 7.1 TFLOPS on the Quadro RTX 4000: this translates to faster neural network training and inference in machine learning pipelines. Similarly, FP16 performance at 10.1 TFLOPS on the RTX 2080 accelerates mixed-precision training, reducing computation time compared to the Quadro's 7.1 TFLOPS.
Memory bandwidth stands out as a key differentiator: 616 GB/s on the RTX 2080 supports larger batch sizes in memory-intensive tasks like large model inference, minimizing data transfer bottlenecks that limit the Quadro RTX 4000's 416 GB/s. For training, higher bandwidth on the RTX 2080 enables processing bigger datasets without swapping to host memory.
Power draw affects deployment: the Quadro RTX 4000's 160W TDP fits low-power clusters better than the RTX 2080's 215W. NVLink on the RTX 2080 enhances multi-GPU scaling for distributed training, unavailable on the Quadro.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
Quadro RTX 4000
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Paperspace | NVIDIA Quadro RTX 4000 8GB VRAM | 8GB | 8 vCPU 30GB RAM 50GB Storage | New York | $0.56/GPU/hr | Available | ||
Paperspace | NVIDIA Quadro RTX 4000 8GB VRAM | 8GB | 8 vCPU 30GB RAM 50GB Storage | Canada | $0.56/GPU/hr | Available | ||
Paperspace | 2×NVIDIA Quadro RTX 4000 8GB VRAM | 8GB | 16 vCPU 60GB RAM 50GB Storage | New York | $0.56/GPU/hr $1.12/hr total (2×) | Available | ||
Paperspace | NVIDIA Quadro RTX 4000 8GB VRAM | 8GB | 8 vCPU 30GB RAM 50GB Storage | Amsterdam | $0.56/GPU/hr | Available | ||
Paperspace | 2×NVIDIA Quadro RTX 4000 8GB VRAM | 8GB | 16 vCPU 60GB RAM 50GB Storage | Canada | $0.56/GPU/hr $1.12/hr total (2×) | Available |
RTX 2080
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Vast.ai | NVIDIA GeForce RTX 2080 Ti 11GB VRAM | 11GB | 32 vCPU 63GB RAM 1273GB Storage | Maryland | $0.13/GPU/hr | Available |
When to Choose the Quadro RTX 4000
The Quadro RTX 4000 suits power-constrained cloud instances where 160W TDP avoids exceeding thermal limits, unlike the RTX 2080's 215W. Professional workflows requiring NVIDIA-certified drivers for stability in CAD or simulation benefit from its design, despite 7.1 TFLOPS FP32 performance.
When to Choose the RTX 2080
The RTX 2080 excels in cost-effective compute: at $0.10 per hour average versus $0.56 for the Quadro RTX 4000, it delivers 10.1 TFLOPS FP32 and 616 GB/s bandwidth for high-volume training or inference. NVLink enables efficient multi-GPU setups for scaled workloads.
Use Cases
The RTX 2080's 10.1 TFLOPS FP16 and 616 GB/s bandwidth handle large models faster than the Quadro RTX 4000's 7.1 TFLOPS and 416 GB/s. Lower $0.10 per hour cost supports extended training sessions.
Higher 10.1 TFLOPS FP16 on RTX 2080 boosts throughput for real-time inference over Quadro's 7.1 TFLOPS. 616 GB/s bandwidth accommodates bigger batches.
RTX 2080's superior 10.1 TFLOPS FP32 accelerates fine-tuning iterations compared to 7.1 TFLOPS on Quadro RTX 4000. Cost savings at $0.10 per hour average make it ideal for iterative work.
10.1 TFLOPS FP16 and up to 11 GB VRAM on RTX 2080 generate images quicker than Quadro's 8 GB and 7.1 TFLOPS. NVLink aids multi-GPU rendering.
Quadro RTX 4000's 160W TDP fits low-power simulations; RTX 2080's 10.1 TFLOPS FP32 handles compute-heavy tasks better. Choice depends on power budget and budget.
Frequently Asked Questions
Which GPU has higher performance?▾
The RTX 2080 delivers 10.1 TFLOPS in FP16 and FP32, surpassing the Quadro RTX 4000's 7.1 TFLOPS by 42 percent. This benefits compute-intensive workloads like training.
What is the memory bandwidth difference?▾
RTX 2080 offers 616 GB/s, 48 percent higher than Quadro RTX 4000's 416 GB/s. Higher bandwidth supports larger batch sizes in ML inference.
How do prices compare in the cloud?▾
RTX 2080 averages $0.10 per hour across 8 offers, versus $0.56 per hour for Quadro RTX 4000 across 5 offers. RTX 2080 provides better value for performance.
Does either support NVLink?▾
RTX 2080 includes NVLink for multi-GPU interconnect, enabling faster scaling in distributed training. Quadro RTX 4000 lacks this feature.
Which has lower power consumption?▾
Quadro RTX 4000 uses 160W TDP, lower than RTX 2080's 215W. It suits power-limited environments.
VRAM comparison?▾
Both start at 8 GB GDDR6; RTX 2080 offers up to 11 GB variants. This aids memory-heavy tasks on RTX 2080.
Which is cheaper to rent, the Quadro RTX 4000 or the RTX 2080?▾
Cloud rental prices for both the Quadro RTX 4000 and RTX 2080 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 4000 have compared to the RTX 2080?▾
The Quadro RTX 4000 has 8 GB of GDDR6 memory. The RTX 2080 has 8 to 11 GB of GDDR6 memory.
Can I find Quadro RTX 4000 and RTX 2080 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 4000 and the RTX 2080?▾
The Quadro RTX 4000 uses the Turing architecture (2018) while the RTX 2080 uses Turing (2018). The RTX 2080 delivers 1.4x the FP16 throughput and 1.5x the memory bandwidth of the Quadro RTX 4000.