Specifications Compared
| Spec | RTX-PRO-6000-BLACKWELL | V100 |
|---|---|---|
| TDP | 400W | 300W |
| VRAM | 96 GB | 16-32 GB |
| CUDA Cores | 21,760 | 5,120 |
| Memory Type | GDDR7 | HBM2 |
| Architecture | Blackwell | Volta |
| Form Factors | PCIe | SXM2, PCIe |
| Interconnect | NVLink | NVLink, PCIe 3.0 |
| Tensor Cores | 680 | 640 |
| FP8 Performance | 2,000 TFLOPS | |
| FP16 Performance | 125 TFLOPS | 125 TFLOPS |
| FP32 Performance | 125 TFLOPS | 15.7 TFLOPS |
| INT8 Performance | 2,000 TOPS | |
| Memory Bandwidth | 1,792 GB/s | 900 GB/s |
Performance Analysis
Both GPUs achieve 125 TFLOPS in FP16 performance, suiting half-precision inference and training. However, the RTX PRO 6000 delivers 125 TFLOPS in FP32, eight times the V100's 15.7 TFLOPS: this disparity accelerates FP32-dominant workloads like scientific simulations and certain training phases. The RTX PRO 6000's 2000 TFLOPS FP8 capability further boosts low-precision inference efficiency.
Memory specifications transform real-world usage. The RTX PRO 6000's 1792 GB/s bandwidth doubles the V100's 900 GB/s, enabling larger batch sizes in deep learning without bottlenecks. Its 96 GB VRAM dwarfs the V100's 16-32 GB, allowing single-GPU handling of models exceeding 32 GB, reducing multi-GPU complexity and latency.
Power draw reflects efficiency gains: the RTX PRO 6000 at 400W TDP versus V100's 300W supports denser deployments via PCIe and NVLink, while V100 offers SXM2 flexibility. These factors make the RTX PRO 6000 ideal for memory-intensive training, where V100 limits scale.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
V100
| 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 RTX PRO 6000
Select the RTX PRO 6000 for workloads demanding over 32 GB VRAM, such as training large language models: its 96 GB GDDR7 capacity avoids model sharding. High FP32 performance at 125 TFLOPS excels in simulations, and 2000 TFLOPS FP8 accelerates inference at scale.
Cloud users prioritizing future-proofing choose it despite $1.25 per hour average, as 1792 GB/s bandwidth sustains massive batches on NVLink interconnects.
When to Choose the V100
The V100 suits cost-sensitive projects with models under 32 GB HBM2 VRAM: at $0.10 per hour starting price, it delivers 125 TFLOPS FP16 for basic inference. Legacy software optimized for Volta benefits from abundant 72 cloud offers averaging $0.94 per hour.
It fits intermittent or experimental tasks where 900 GB/s bandwidth and 300W TDP suffice without needing FP32 boosts.
Use Cases
The RTX PRO 6000's 96 GB VRAM and 125 TFLOPS FP32 handle large models without splitting, unlike V100's 16-32 GB limit.
2000 TFLOPS FP8 on RTX PRO 6000 accelerates high-throughput serving; 1792 GB/s bandwidth supports bigger batches than V100's 900 GB/s.
96 GB VRAM fits full parameter sets for fine-tuning large models; 125 TFLOPS FP32 speeds iterations over V100's 15.7 TFLOPS.
V100's 125 TFLOPS FP16 suffices for standard resolutions at low cost; RTX PRO 6000's extra VRAM aids high-res batch generation.
RTX PRO 6000's 125 TFLOPS FP32 provides eightfold speedup over V100's 15.7 TFLOPS for simulations.
Frequently Asked Questions
What is the VRAM difference between RTX PRO 6000 and V100?▾
The RTX PRO 6000 has 96 GB GDDR7 VRAM, while the V100 offers 16-32 GB HBM2. This enables the RTX PRO 6000 to manage models three to six times larger without multi-GPU setups.
How do FP32 performances compare?▾
RTX PRO 6000 achieves 125 TFLOPS FP32, compared to V100's 15.7 TFLOPS. This results in approximately eight times faster single-precision compute for training and simulations.
What are the current cloud prices?▾
RTX PRO 6000 pricing starts at $0.59 per hour, averaging $1.25 per hour across five offers. V100 starts at $0.10 per hour, averaging $0.94 per hour across 72 offers.
Which has higher memory bandwidth?▾
RTX PRO 6000 provides 1792 GB/s, nearly double the V100's 900 GB/s. Higher bandwidth supports larger batch sizes in deep learning workloads.
What architectures do they use?▾
RTX PRO 6000 uses 2025 Blackwell architecture with FP8 at 2000 TFLOPS. V100 employs 2017 Volta with NVLink and PCIe 3.0 interconnects.
How do TDPs compare?▾
RTX PRO 6000 has a 400W TDP, higher than V100's 300W. This reflects greater compute density in PCIe form factor for the newer GPU.
Which is cheaper to rent, the RTX PRO 6000 or the V100?▾
Cloud rental prices for both the RTX PRO 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 RTX PRO 6000 have compared to the V100?▾
The RTX PRO 6000 has 96 GB of GDDR7 memory. The V100 has 16 to 32 GB of HBM2 memory.
Can I find RTX PRO 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 RTX PRO 6000 and the V100?▾
The RTX PRO 6000 uses the Blackwell architecture (2025) while the V100 uses Volta (2017). The V100 delivers 1.0x the FP16 throughput and 2.0x the memory bandwidth of the RTX PRO 6000.