Specifications Compared
| Spec | RTX-4080 | V100 |
|---|---|---|
| TDP | 320W | 300W |
| VRAM | 16 GB | 16-32 GB |
| CUDA Cores | 9,728 | 5,120 |
| Memory Type | GDDR6X | HBM2 |
| Architecture | Ada Lovelace | Volta |
| Form Factors | PCIe | SXM2, PCIe |
| Interconnect | NVLink, PCIe 3.0 | |
| Tensor Cores | 304 | 640 |
| FP16 Performance | 48.7 TFLOPS | 125 TFLOPS |
| FP32 Performance | 48.7 TFLOPS | 15.7 TFLOPS |
| INT8 Performance | 780 TOPS | |
| Memory Bandwidth | 717 GB/s | 900 GB/s |
Performance Analysis
Key spec differences translate to distinct real-world behaviors in AI workloads. The V100's 125 TFLOPS FP16 rating excels in mixed-precision training, where tensor cores accelerate matrix operations common in deep learning frameworks like TensorFlow and PyTorch; its 900 GB/s HBM2 bandwidth supports larger batch sizes without bottlenecks, ideal for models up to 16 GB. Conversely, the RTX 4080 SUPER's balanced 48.7 TFLOPS across FP16 and FP32 suits inference and single-precision tasks, where FP32 dominance at 48.7 TFLOPS outperforms the V100's 15.7 TFLOPS, enabling faster general-purpose computing. Memory bandwidth impacts batch sizes directly: the V100's 900 GB/s handles bigger batches in training loops, reducing iterations, while the RTX 4080 SUPER's 717 GB/s suffices for optimized modern models with quantization. Newer Ada Lovelace architecture in the RTX 4080 SUPER brings efficiency gains in CUDA 12+ ecosystems, lowering effective compute costs despite lower peak FP16. For inference, the RTX 4080 SUPER processes FP32-heavy pipelines quicker, but V100 thrives in FP16-dominant legacy training.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
RTX 4080 SUPER
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 RunPod | NVIDIA GeForce RTX 4080 SUPER 16GB VRAM | 16GB | 6 vCPU 35GB RAM | 🌍global | $0.50/GPU/hr | |||
RunPod | NVIDIA GeForce RTX 4080 16GB VRAM | 16GB | 6 vCPU 35GB RAM | 🌍global | $0.50/GPU/hr |
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 4080 SUPER
The RTX 4080 SUPER stands out for modern inference pipelines and FP32-intensive simulations. Its 48.7 TFLOPS FP32 throughput triples the V100's 15.7 TFLOPS, accelerating tasks like scientific visualizations or gaming-related AI. At an average cloud price of $0.32 per hour, it offers better value for workloads leveraging Ada Lovelace optimizations in recent frameworks.
When to Choose the Tesla V100 16GB
Opt for the V100 16GB in high-FP16 training scenarios demanding peak tensor performance. Its 125 TFLOPS FP16 dwarfs the RTX 4080 SUPER's 48.7 TFLOPS, speeding up large-model training with 900 GB/s bandwidth for massive batches. With 26 live cloud offers starting at $0.10 per hour, it provides unmatched affordability for legacy Volta-compatible codebases.
Use Cases
The V100's 125 TFLOPS FP16 and 900 GB/s bandwidth enable faster training of large language models with bigger batches. It outperforms the RTX 4080 SUPER's 48.7 TFLOPS FP16 in mixed-precision setups.
The RTX 4080 SUPER's balanced 48.7 TFLOPS FP32 supports efficient quantized inference. Its newer architecture handles real-time serving better than the V100's 15.7 TFLOPS FP32.
Both GPUs offer 16 GB VRAM for fine-tuning mid-sized models. Choose V100 for FP16-heavy tasks at $0.10 per hour low; RTX 4080 SUPER for FP32 balance at $0.17 per hour.
RTX 4080 SUPER's 48.7 TFLOPS FP32 excels in diffusion model generation. Ada Lovelace optimizations yield faster image synthesis than V100's weaker FP32.
The RTX 4080 SUPER's 48.7 TFLOPS FP32 triples V100's 15.7 TFLOPS for simulations. PCIe form factor simplifies integration in diverse compute environments.
Frequently Asked Questions
Which GPU has higher FP16 performance?▾
The V100 delivers 125 TFLOPS FP16, far exceeding the RTX 4080 SUPER's 48.7 TFLOPS. This makes V100 preferable for tensor-heavy training. Bandwidth at 900 GB/s further aids V100 in memory-bound tasks.
What is the memory bandwidth comparison?▾
V100 provides 900 GB/s with HBM2, surpassing RTX 4080 SUPER's 717 GB/s GDDR6X. Higher bandwidth on V100 supports larger batch sizes in training. Both have 16 GB VRAM capacity.
Which is cheaper in the cloud?▾
V100 16GB starts at $0.10 per hour across 26 offers, cheaper than RTX 4080 SUPER's $0.17 per hour from 3 offers. Averages are $0.82 per hour for V100 versus $0.32 per hour for RTX 4080 SUPER.
Does RTX 4080 SUPER have better FP32?▾
RTX 4080 SUPER achieves 48.7 TFLOPS FP32, over three times V100's 15.7 TFLOPS. This benefits FP32-dominant inference and simulations. Balanced performance suits general workloads.
What are the power requirements?▾
RTX 4080 SUPER draws 320 W TDP, slightly more than V100's 300 W. Both fit standard cloud instances efficiently. PCIe form factor is common to both.
Which architecture is newer?▾
RTX 4080 SUPER uses 2022 Ada Lovelace, versus V100's 2017 Volta. Newer architecture supports latest CUDA features. V100 remains viable for legacy code.
Which is cheaper to rent, the RTX 4080 or the V100?▾
Cloud rental prices for both the RTX 4080 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 4080 have compared to the V100?▾
The RTX 4080 has 16 GB of GDDR6X memory. The V100 has 16 to 32 GB of HBM2 memory.
Can I find RTX 4080 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 4080 and the V100?▾
The RTX 4080 uses the Ada Lovelace architecture (2022) while the V100 uses Volta (2017). The V100 delivers 2.6x the FP16 throughput and 1.3x the memory bandwidth of the RTX 4080.