NVIDIA Tesla V100
5,120
CUDA Cores
32GB
VRAM
900
GB/s
Technical Specifications
5,120
CUDA Cores
1245
Base MHz
1455
Boost MHz
32GB HBM2
4096-bit bus
Performance
14
FP32 TFLOPS
112
FP16 TFLOPS
300W
TDP
Cloud Availability
2
Available Instances
$0.18/hr
Starting Price
Detailed Specifications
| Architecture | Volta (Unknown) |
| Release Date | 2017-06-20 |
| Launch Price | $10,000.00 |
| Process | 12nm |
| Transistors | 21.1B |
AI Features
Gen 1
Tensor Cores
Disabled
Transformer Engine
Not Supported
Flash Attention
Physical Specifications
Dimensions
10.5in
Length
4.4in
Width
2-slot
Height
About Tesla V100 GPU
The NVIDIA Tesla V100 was introduced in 2017 on the Volta architecture. Its specification combines 5,120 CUDA cores with 32GB of HBM2, which defines both the workloads it can run and the batch sizes it can hold.
Based on its 32GB memory capacity, the Tesla V100 is best evaluated for model fine-tuning, production inference, image generation, and multi-GPU development. Always size the model, optimizer state, KV cache, and framework overhead together rather than choosing a GPU from core count alone.
GPUvec currently tracks 2 cloud listings for the Tesla V100, with the lowest observed hourly rate at $0.18. Prices and availability can change by provider, region, and instance configuration, so verify the final rate before starting a workload.
For software planning, the Tesla V100 combines 900 GB/s of memory bandwidth, CUDA compute capability the latest, and a 12nm manufacturing process. Confirm the minimum CUDA, PyTorch, TensorFlow, or driver version required by your application before renting or purchasing hardware.
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External Resources
Learn more about GPUs from these authoritative sources:
Official CUDA programming guide
NVIDIA GPU Specifications →Official NVIDIA GPU specs
TechPowerUp GPU Database →Comprehensive GPU specifications
CUDA Compute Capability Guide →GPU compute capability reference
What You Need to Know About the Tesla V100
Complete Specifications for the NVIDIA Tesla V100
The NVIDIA Tesla V100 uses the Volta architecture and combines 5,120 CUDA cores with 32GB of HBM2. GPUvec records 900 GB/s memory bandwidth alongside architecture, release date, process node, accelerator features, and physical specifications so the page can be evaluated from source data instead of model-name assumptions.
Compare NVIDIA Tesla V100 Cloud Rental Prices per Hour
GPUvec currently tracks 2 cloud listings, with an observed starting rate of $0.18 per hour. Provider rates can vary by region, host configuration, billing model, and availability. Use the provider directory and related-GPU links to compare an available alternative when this exact accelerator is not listed.
Is the NVIDIA Tesla V100 the Right GPU for Your AI Workload?
Start with the 32GB memory limit, then evaluate 900 GB/s memory bandwidth, compute features, and software compatibility for your workload. Model weights, KV cache, activations, optimizer state, and framework overhead all consume memory. Compare this page with related accelerators rather than assuming that a higher core count alone guarantees a better training or inference result.
Top GPUs for Training and Inference
| Category | Rank 1 | Rank 2 | Rank 3 |
|---|---|---|---|
| Best for Training | NVIDIA H200 | NVIDIA H100 | NVIDIA B200 |
| Best for Inference | NVIDIA A40 | NVIDIA A100 | NVIDIA A10 |
Compare GPU specifications and cloud instances to find the best GPU for your workload.