Specifications Compared
| Spec | QUADRO-RTX-8000 | V100 |
|---|---|---|
| TDP | 260W | 300W |
| VRAM | 48 GB | 16-32 GB |
| CUDA Cores | 4,608 | 5,120 |
| Memory Type | GDDR6 | HBM2 |
| Architecture | Turing | Volta |
| Form Factors | PCIe | SXM2, PCIe |
| Interconnect | NVLink | NVLink, PCIe 3.0 |
| Tensor Cores | 576 | 640 |
| FP16 Performance | 16.3 TFLOPS | 125 TFLOPS |
| FP32 Performance | 16.3 TFLOPS | 15.7 TFLOPS |
| Memory Bandwidth | 672 GB/s | 900 GB/s |
Performance Analysis
The FP16 performance gap defines key workloads: the V100 achieves 125 TFLOPS, enabling eight times faster mixed-precision training than the RTX 8000's 16.3 TFLOPS. This benefits deep learning training where tensor cores accelerate matrix operations. FP32 rates are nearly identical at 16.3 TFLOPS for RTX 8000 and 15.7 TFLOPS for V100, suiting single-precision scientific simulations equally. Memory bandwidth favors the V100 at 900 GB/s over 672 GB/s, supporting larger batch sizes in training by reducing data transfer bottlenecks. The RTX 8000 counters with 48 GB GDDR6 VRAM versus 16 GB HBM2, allowing bigger models or datasets in inference without swapping to host memory. Higher TDP at 300 W for V100 reflects its compute focus, while RTX 8000's 260 W suits power-constrained workstations. Overall, V100 excels in compute-bound AI training; RTX 8000 thrives in VRAM-limited inference.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
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 8000
The Quadro RTX 8000 suits visualization-heavy workflows or large-model inference requiring 48 GB VRAM. Its GDDR6 capacity handles high-resolution rendering or Stable Diffusion tasks without memory constraints, unlike the V100's 16 GB limit. Lower 260 W TDP fits dense workstation deployments. Professionals in CAD or graphics prefer it for PCIe flexibility and Turing RT cores absent in Volta.
When to Choose the Tesla V100 16GB
The Tesla V100 16 GB dominates AI training and HPC due to 125 TFLOPS FP16 performance. Cloud availability from $0.10 per hour across 27 offers makes it economical for scale-out jobs. Superior 900 GB/s bandwidth supports memory-intensive simulations better than RTX 8000's 672 GB/s.
Use Cases
V100's 125 TFLOPS FP16 outperforms RTX 8000's 16.3 TFLOPS for mixed-precision training. Higher 900 GB/s bandwidth handles large batches efficiently.
RTX 8000's 48 GB VRAM supports larger models than V100's 16 GB. FP32 parity at 16.3 TFLOPS versus 15.7 TFLOPS ensures comparable speed.
V100 excels with 125 TFLOPS FP16 for rapid iterations. Affordable cloud access from $0.10 per hour suits experimentation.
48 GB VRAM on RTX 8000 enables high-resolution generation without limits. Turing architecture boosts ray-tracing elements.
V100's 900 GB/s bandwidth and 125 TFLOPS FP16 accelerate simulations. NVLink scales multi-GPU HPC clusters effectively.
Frequently Asked Questions
Which GPU has more VRAM?▾
The Quadro RTX 8000 provides 48 GB GDDR6 VRAM. This triples the Tesla V100 16 GB's HBM2 capacity. Larger VRAM benefits memory-bound inference tasks.
What is the FP16 performance difference?▾
V100 delivers 125 TFLOPS FP16, vastly exceeding RTX 8000's 16.3 TFLOPS. This gap favors V100 in tensor-accelerated training. FP32 remains close at 15.7 TFLOPS versus 16.3 TFLOPS.
How do memory bandwidths compare?▾
V100 offers 900 GB/s with HBM2, surpassing RTX 8000's 672 GB/s GDDR6. Higher bandwidth on V100 supports bigger batches in training. RTX 8000 compensates with more VRAM.
What are the power requirements?▾
RTX 8000 consumes 260 W TDP, lower than V100's 300 W. This makes RTX 8000 preferable for power-sensitive setups. Both support NVLink for multi-GPU.
Is V100 available in the cloud?▾
Tesla V100 16 GB has 27 live cloud offers from $0.10 per hour, averaging $0.82 per hour. RTX 8000 lacks current live offers. Cloud access favors V100 for scalable workloads.
Which is newer?▾
Quadro RTX 8000 uses 2018 Turing architecture, postdating V100's 2017 Volta. Turing adds RT cores for graphics. Volta prioritizes tensor compute.
Which is cheaper to rent, the Quadro RTX 8000 or the V100?▾
Cloud rental prices for both the Quadro RTX 8000 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 8000 have compared to the V100?▾
The Quadro RTX 8000 has 48 GB of GDDR6 memory. The V100 has 16 to 32 GB of HBM2 memory.
Can I find Quadro RTX 8000 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 8000 and the V100?▾
The Quadro RTX 8000 uses the Turing architecture (2018) while the V100 uses Volta (2017). The V100 delivers 7.7x the FP16 throughput and 1.3x the memory bandwidth of the Quadro RTX 8000.