NVIDIA Tesla P100
3,584
CUDA Cores
16GB
VRAM
732
GB/s
Technical Specifications
3,584
CUDA Cores
1190
Base MHz
1328
Boost MHz
16GB HBM2
4096-bit bus
Performance
9.3
FP32 TFLOPS
18.7
FP16 TFLOPS
250W
TDP
Cloud Availability
0
Available Instances
$0.00/hr
Starting Price
Detailed Specifications
| Architecture | Pascal (Unknown) |
| Release Date | 2016-06-20 |
| Launch Price | $10,000.00 |
| Process | 16nm |
| Transistors | 15.3B |
AI Features
none
Tensor Cores
Disabled
Transformer Engine
Not Supported
Flash Attention
Physical Specifications
Dimensions
10.5in
Length
4.4in
Width
2-slot
Height
About Tesla P100 GPU
The NVIDIA Tesla P100 was introduced in 2016 on the Pascal architecture. Its specification combines 3,584 CUDA cores with 16GB of HBM2, which defines both the workloads it can run and the batch sizes it can hold.
Based on its 16GB memory capacity, the Tesla P100 is best evaluated for quantized language-model inference, image generation, and medium-sized training jobs. Always size the model, optimizer state, KV cache, and framework overhead together rather than choosing a GPU from core count alone.
GPUvec does not currently have an active hourly cloud listing for the Tesla P100. This is not a zero-dollar offer: it means no comparable provider price is available in the present dataset. Check the provider directory for availability or compare a related GPU with an active listing.
For software planning, the Tesla P100 combines 732 GB/s of memory bandwidth, CUDA compute capability the latest, and a 16nm manufacturing process. Confirm the minimum CUDA, PyTorch, TensorFlow, or driver version required by your application before renting or purchasing hardware.
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 P100
Complete Specifications for the NVIDIA Tesla P100
The NVIDIA Tesla P100 uses the Pascal architecture and combines 3,584 CUDA cores with 16GB of HBM2. GPUvec records 732 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 P100 Cloud Rental Prices per Hour
GPUvec currently has no comparable active hourly listing for this model; this means pricing is unavailable, not free. 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 P100 the Right GPU for Your AI Workload?
Start with the 16GB memory limit, then evaluate 732 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.