Specifications Compared
| Spec | RTX-2060 | V100 |
|---|---|---|
| TDP | 160W | 300W |
| VRAM | 6-12 GB | 16-32 GB |
| CUDA Cores | 1,920 | 5,120 |
| Memory Type | GDDR6 | HBM2 |
| Architecture | Turing | Volta |
| Form Factors | PCIe | SXM2, PCIe |
| Interconnect | NVLink, PCIe 3.0 | |
| Tensor Cores | 240 | 640 |
| FP16 Performance | 6.5 TFLOPS | 125 TFLOPS |
| FP32 Performance | 6.5 TFLOPS | 15.7 TFLOPS |
| Memory Bandwidth | 336 GB/s | 900 GB/s |
Performance Analysis
Compute disparity defines these GPUs: V100 achieves 125 TFLOPS FP16 versus 7.2 TFLOPS on RTX 2060 SUPER, enabling up to 17 times faster deep learning training and inference in half-precision formats common today. FP32 performance follows at 15.7 TFLOPS for V100 against 7.2 TFLOPS, benefiting general simulations and single-precision workloads.
Memory differences impact real-world use profoundly. V100s 900 GB/s bandwidth and 16 GB HBM2 handle large batch sizes without stalling, ideal for training transformers exceeding 8 GB VRAM limits of RTX 2060 SUPERs 448 GB/s GDDR6. This allows V100 to process bigger datasets efficiently.
Power profiles contrast sharply: RTX 2060 SUPERs 175 W TDP suits edge deployments, yielding better efficiency for small-scale inference, whereas V100s 300 W prioritizes peak performance over consumption.
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 RTX 2060 SUPER
The RTX 2060 SUPER fits scenarios with small models under 8 GB VRAM, such as lightweight inference or Stable Diffusion at 512x512 resolutions. Its 7.2 TFLOPS FP32 and 448 GB/s bandwidth deliver adequate speed for consumer tasks like gaming-assisted ML prototyping. The 175 W TDP minimizes cooling and power costs in personal or edge setups lacking cloud availability.
When to Choose the Tesla V100 16GB
Select V100 16GB for intensive workloads demanding high throughput, like LLM training with 125 TFLOPS FP16 and 16 GB VRAM. Its 900 GB/s bandwidth supports massive batches in fine-tuning or scientific computing. Cloud access from $0.10 per hour scales compute without upfront hardware investment.
Use Cases
V100s 125 TFLOPS FP16 and 16 GB HBM2 VRAM manage large-scale training batches far beyond RTX 2060 SUPERs 7.2 TFLOPS and 8 GB GDDR6.
High 125 TFLOPS FP16 throughput on V100 accelerates token generation; 900 GB/s bandwidth prevents bottlenecks in serving.
16 GB VRAM and 900 GB/s bandwidth enable larger models and batches during fine-tuning compared to RTX 2060 SUPERs constraints.
RTX 2060 SUPERs 8 GB VRAM and Turing tensor cores fit standard pipelines efficiently for image generation at modest scales.
V100s 15.7 TFLOPS FP32 and HBM2 memory excel in high-bandwidth simulations outpacing RTX 2060 SUPER.
Frequently Asked Questions
Does V100 16GB have more VRAM than RTX 2060 SUPER?▾
V100 16GB offers 16 GB HBM2 VRAM, twice the 8 GB GDDR6 in RTX 2060 SUPER. Bandwidth reaches 900 GB/s on V100 versus 448 GB/s. This supports larger AI models without swapping.
Is V100 better for deep learning than RTX 2060 SUPER?▾
V100 dominates with 125 TFLOPS FP16 versus 7.2 TFLOPS, up to 17x faster training. FP32 stands at 15.7 TFLOPS against 7.2 TFLOPS. Memory aids large workloads.
What are the TDP ratings for RTX 2060 SUPER and V100?▾
RTX 2060 SUPER consumes 175 W TDP, lower than V100s 300 W. SUPER suits power-limited setups. V100 prioritizes performance density.
Are there cloud rentals for RTX 2060 SUPER versus V100?▾
No live offers exist for RTX 2060 SUPER currently. V100 16GB starts at $0.10 per hour, averaging $0.82 per hour over 27 providers.
Which GPU for Stable Diffusion: RTX 2060 SUPER or V100?▾
RTX 2060 SUPER handles Stable Diffusion well within 8 GB VRAM for typical resolutions. V100 generates faster via 125 TFLOPS FP16 but costs more.
Volta or Turing: which architecture wins here?▾
Turing powers RTX 2060 SUPER from 2019, newer than V100s 2017 Volta. V100s datacenter specs deliver 125 TFLOPS FP16 over SUPERs 7.2 TFLOPS for ML.
Which is cheaper to rent, the RTX 2060 or the V100?▾
Cloud rental prices for both the RTX 2060 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 2060 have compared to the V100?▾
The RTX 2060 has 6 to 12 GB of GDDR6 memory. The V100 has 16 to 32 GB of HBM2 memory.
Can I find RTX 2060 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 2060 and the V100?▾
The RTX 2060 uses the Turing architecture (2019) while the V100 uses Volta (2017). The V100 delivers 19.2x the FP16 throughput and 2.7x the memory bandwidth of the RTX 2060.