Loading...
The NVIDIA Tesla P100 was the first GPU to feature HBM2 memory, delivering exceptional memory bandwidth for its era. While now superseded by V100, A100, and newer GPUs, the P100 remains available on some cloud platforms at lower prices for budget-conscious workloads. For CUDA developers, the P100 offers solid FP16 performance and NVLink support but lacks Tensor Cores found in newer GPUs. Its Compute Capability 6.0 is still supported by most frameworks but may not receive future optimizations. This guide covers the P100's specifications, optimization strategies, and practical considerations for whether to use this legacy GPU.
| Architecture | Pascal (GP100) |
| CUDA Cores | 3,584 |
| Tensor Cores | 0 |
| Memory | 16GB HBM2 |
| Memory Bandwidth | 732 GB/s |
| Base / Boost Clock | 1126 / 1480 MHz |
| FP32 Performance | 10.6 TFLOPS |
| FP16 Performance | 21.2 TFLOPS |
| L2 Cache | 4MB |
| TDP | 300W |
| NVLink | Yes |
| MSRP | $6,000 |
| Release | June 2016 |
This code snippet shows how to detect your P100, check available memory, and configure optimal settings for the Pascal (GP100) architecture.
import torch
import pynvml
# Check if P100 is available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Using device: {torch.cuda.get_device_name(0)}")
# P100 Memory: 16GB - Optimal batch sizes
# Architecture: Pascal (GP100)
# CUDA Cores: 3,584
# Memory-efficient training for P100
torch.backends.cuda.matmul.allow_tf32 = True # Enable TF32 for Pascal (GP100)
torch.backends.cudnn.allow_tf32 = True
# Check available memory
pynvml.nvmlInit()
handle = pynvml.nvmlDeviceGetHandleByIndex(0)
info = pynvml.nvmlDeviceGetMemoryInfo(handle)
print(f"Free memory: {info.free / 1024**3:.1f} GB / 16 GB total")
# Recommended batch size calculation for P100
model_memory_gb = 2.0 # Adjust based on your model
batch_multiplier = (16 - model_memory_gb) / 4 # 4GB per batch unit
recommended_batch = int(batch_multiplier * 32)
print(f"Recommended batch size for P100: {recommended_batch}")| Task | Performance | Comparison |
|---|---|---|
| ResNet-50 Training (imgs/sec) | 280 | FP16 mode |
| BERT Inference (sentences/sec) | 250 | No Tensor Cores |
| cuBLAS SGEMM (TFLOPS) | 10.2 | 96% efficiency |
| FP64 GFLOPS | 5,300 | Strong for HPC |
| Memory Bandwidth (GB/s) | 700 | 96% efficiency |
| NVLink Bandwidth (GB/s) | 160 | First gen NVLink |
| Use Case | Rating | Notes |
|---|---|---|
| Scientific Computing | Good | Strong FP64, but V100 better |
| ML Training | Fair | No Tensor Cores hurts performance |
| Legacy Workloads | Good | Cost-effective for existing code |
| Budget Cloud | Good | Lower prices on some clouds |
| Modern ML | Poor | Lacks modern features |
| Inference | Fair | T4 or V100 much better |
Only for legacy workloads or extreme budget constraints. Modern GPUs like T4, A10, or V100 offer dramatically better price/performance for ML. P100 is approaching end-of-life for framework support.
V100 is 2-3x faster for ML due to Tensor Cores, has 32GB memory option, and better architecture. P100 only makes sense if V100 is unavailable or significantly more expensive.
P100 with Compute Capability 6.0 is still supported by CUDA 12 and major frameworks, but may lose support in future versions. Some frameworks already recommend CC 7.0+ for optimal performance.
P100 has good FP64 performance at 5.3 TFLOPS. However, V100 and A100 offer significantly better FP64 along with Tensor Cores. For pure FP64, P100 may be cost-effective if available.
3x faster with Tensor Cores
Lower power, better inference
Modern Ampere, much faster
Consumer, Tensor Cores
Ready to optimize your CUDA kernels for P100? Download RightNow AI for real-time performance analysis.