Specifications Compared
| Spec | QUADRO-RTX-6000 | V100 |
|---|---|---|
| TDP | 260W | 300W |
| VRAM | 24 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
FP16 performance reveals the primary divergence: the V100 delivers 125 TFLOPS versus the Quadro RTX 6000's 16.3 TFLOPS, enabling up to 7.7 times faster deep learning training and inference in half-precision formats common in modern AI pipelines. FP32 rates stay comparable at 15.7 TFLOPS for V100 and 16.3 TFLOPS for Quadro RTX 6000, supporting similar throughput in single-precision scientific simulations or graphics rendering. This balance positions the Quadro for mixed workloads, while V100 accelerates tensor core-optimized neural networks. Memory bandwidth favors V100 at 900 GB/s over 672 GB/s, permitting larger batch sizes in training loops and reducing data transfer bottlenecks by 34 percent. Quadro RTX 6000 counters with 24 GB GDDR6 VRAM against 16 GB HBM2, accommodating bigger models or higher-resolution datasets in inference or visualization without out-of-memory errors. Higher 300W TDP on V100 reflects its datacenter density, contrasting Quadro's efficient 260W for workstations.
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 6000
The Quadro RTX 6000 suits professional visualization and CAD workflows: its 24 GB GDDR6 VRAM manages complex scenes exceeding the V100's 16 GB HBM2 capacity. Lower 260W TDP integrates easily into workstation builds, and balanced 16.3 TFLOPS across FP16 and FP32 handles rendering or simulation without FP16 specialization.
When to Choose the Tesla V100 16GB
Data center AI training favors the V100: 125 TFLOPS FP16 outperforms Quadro RTX 6000's 16.3 TFLOPS, speeding half-precision computations essential for neural networks. Superior 900 GB/s bandwidth supports larger batches, and cloud availability from $0.10 per hour enables scalable deployments unavailable for Quadro RTX 6000.
Use Cases
V100's 125 TFLOPS FP16 vastly exceeds Quadro RTX 6000's 16.3 TFLOPS, accelerating large language model training in half-precision. Higher 900 GB/s bandwidth handles massive datasets efficiently.
Quadro RTX 6000's 24 GB VRAM supports larger models than V100's 16 GB without swapping. Balanced 16.3 TFLOPS FP32 suits real-time serving.
V100's 125 TFLOPS FP16 optimizes fine-tuning iterations far beyond Quadro RTX 6000's 16.3 TFLOPS. 900 GB/s bandwidth enables bigger batches.
Quadro RTX 6000's 24 GB VRAM and Turing architecture manage high-resolution image generation better than V100's 16 GB. 16.3 TFLOPS FP32 aids diffusion steps.
Comparable FP32 at 16.3 TFLOPS for Quadro RTX 6000 and 15.7 TFLOPS for V100 suits simulations equally. Choice depends on VRAM needs: 24 GB versus 16 GB.
Frequently Asked Questions
Which GPU has more VRAM?▾
The Quadro RTX 6000 provides 24 GB GDDR6 VRAM, surpassing the V100 16GB's 16 GB HBM2. This advantage aids memory-intensive visualization or large-model inference. V100's HBM2 offers higher speed but less capacity.
What is the FP16 performance difference?▾
V100 achieves 125 TFLOPS FP16, dwarfing Quadro RTX 6000's 16.3 TFLOPS by a factor of 7.7. This gap accelerates deep learning training. FP32 remains close at 15.7 TFLOPS versus 16.3 TFLOPS.
Which has higher memory bandwidth?▾
V100 delivers 900 GB/s bandwidth from HBM2, exceeding Quadro RTX 6000's 672 GB/s GDDR6 by 34 percent. Larger batches become feasible in training. This benefits compute-bound AI tasks.
What are the power requirements?▾
Quadro RTX 6000 consumes 260W TDP, lower than V100's 300W. Workstations prefer the efficiency. V100 suits dense datacenter racks.
Is cloud pricing available?▾
V100 16GB offers start from $0.10 per hour, averaging $0.81 across 25 providers. Quadro RTX 6000 has no live cloud offers. V100 enables cost-effective scaling.
Which is newer?▾
Quadro RTX 6000 uses 2018 Turing architecture, postdating V100's 2017 Volta. Turing adds ray tracing capabilities. Both serve legacy pro workloads.
Which is cheaper to rent, the Quadro RTX 6000 or the V100?▾
Cloud rental prices for both the Quadro RTX 6000 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 6000 have compared to the V100?▾
The Quadro RTX 6000 has 24 GB of GDDR6 memory. The V100 has 16 to 32 GB of HBM2 memory.
Can I find Quadro RTX 6000 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 6000 and the V100?▾
The Quadro RTX 6000 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 6000.