Rent NVIDIA Tesla V100 32GB Cloud Instances
📊 Pricing at a Glance
NVIDIA Tesla V100 32GB rental pricing ranges from $0.29/GPU/hr to $3.90/GPU/hr across 62 instances from 5 providers (updated June 2026).
Looking for a specific provider? See TensorDock NVIDIA Tesla V100 32GB, Ori NVIDIA Tesla V100 32GB, or Paperspace NVIDIA Tesla V100 32GB.
Available Offers
Compare the top 5 cheapest offers from 5 providers.
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 TensorDock | NVIDIA Tesla V100 32GB 32GB VRAM | 32GB | 0 vCPU 0GB RAM | New York City | $0.29/GPU/hr | Available | ||
TensorDock | NVIDIA Tesla V100 32GB 32GB VRAM | 32GB | 0 vCPU 0GB RAM | Texas | $0.29/GPU/hr | Available | ||
 Ori | NVIDIA Tesla V100 32GB 32GB VRAM | 32GB | 15 vCPU 45GB RAM 300GB Storage | Frankfurt | $0.95/GPU/hr | Available | ||
Ori | NVIDIA Tesla V100 32GB 32GB VRAM | 32GB | 15 vCPU 45GB RAM 300GB Storage | Warsaw | $0.95/GPU/hr | Available | ||
Ori | NVIDIA Tesla V100 32GB 32GB VRAM | 32GB | 15 vCPU 45GB RAM 400GB Storage | Beauharnois | $0.95/GPU/hr | Available |
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Technical Specifications
Strengths & Limitations
- Large 32GB HBM2 memory capacity allows for processing larger datasets and more complex models.
- Excellent double-precision (FP64) performance suitable for scientific computing.
- Tensor Cores accelerate deep learning training and inference.
- High memory bandwidth reduces data transfer bottlenecks.
- Mature and well-supported architecture.
- Higher cost compared to consumer-grade GPUs.
- Lower single-precision (FP32) performance compared to newer generation GPUs like the NVIDIA RTX 3080 ($0.05/hr).
- Power consumption can be significant.
- HBM2 memory, while fast, is more expensive than GDDR6.
- Lacks support for newer features like sparsity acceleration found in Ampere and later architectures.
Top Use Cases
Training large and complex deep learning models, especially those requiring significant memory capacity, such as large language models or high-resolution image processing models.
Accelerating scientific simulations and computations in fields like fluid dynamics, molecular dynamics, and weather forecasting.
Processing and analyzing large datasets in fields like finance, genomics, and marketing.
Real-World Benchmark
Market Analysis
The NVIDIA Tesla V100 32GB remains a viable option for users needing substantial memory capacity for their workloads. While newer GPUs like the NVIDIA A100 PCIe 80GB ($0.12/hr) offer superior performance and features, the V100 32GB can be a cost-effective solution for specific use cases where its memory capacity is critical. It's important to consider the overall cost-performance ratio compared to newer alternatives, especially if the workload is not heavily memory-bound. The availability of newer GPUs has driven down the relative value of the V100.
Frequently Asked Questions
What is the difference between the Tesla V100 16GB and 32GB?â–¾
The primary difference is the memory capacity. The 32GB version can handle larger datasets and more complex models that exceed the 16GB limit of the smaller version. Both have the same core architecture and number of CUDA and Tensor cores.
Is the Tesla V100 32GB suitable for gaming?â–¾
No. The Tesla V100 is designed for data center and HPC workloads, not gaming. It lacks the display outputs and drivers optimized for gaming performance. Consumer-grade GPUs like the GeForce RTX series are much better suited for gaming.
What kind of power supply is required for the Tesla V100 32GB?â–¾
The Tesla V100 typically requires a high-wattage power supply, often 750W or greater, depending on the overall system configuration. Refer to the manufacturer's specifications for the recommended power supply requirements.
Alternative GPUs
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