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The NVIDIA GeForce GTX 1080 Ti remains a capable GPU for CUDA development, offering 11GB VRAM at very low used prices. Built on Pascal architecture, it lacks Tensor Cores but provides solid FP32 compute for traditional CUDA workloads. For CUDA developers on extreme budgets, used GTX 1080 Ti cards offer substantial VRAM and compute power. While unsuitable for modern mixed-precision training, they handle inference, classical ML, and learning tasks effectively. This guide covers realistic expectations and optimization strategies for the GTX 1080 Ti in 2025.
| Architecture | Pascal (GP102) |
| CUDA Cores | 3,584 |
| Tensor Cores | 0 |
| Memory | 11GB GDDR5X |
| Memory Bandwidth | 484 GB/s |
| Base / Boost Clock | 1481 / 1582 MHz |
| FP32 Performance | 11.3 TFLOPS |
| FP16 Performance | 0.18 TFLOPS |
| L2 Cache | 2.75MB |
| TDP | 250W |
| NVLink | No |
| MSRP | $699 |
| Release | March 2017 |
This code snippet shows how to detect your GTX 1080 Ti, check available memory, and configure optimal settings for the Pascal (GP102) architecture.
import torch
import pynvml
# Check if GTX 1080 Ti is available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Using device: {torch.cuda.get_device_name(0)}")
# GTX 1080 Ti Memory: 11GB - Optimal batch sizes
# Architecture: Pascal (GP102)
# CUDA Cores: 3,584
# Memory-efficient training for GTX 1080 Ti
torch.backends.cuda.matmul.allow_tf32 = True # Enable TF32 for Pascal (GP102)
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 / 11 GB total")
# Recommended batch size calculation for GTX 1080 Ti
model_memory_gb = 2.0 # Adjust based on your model
batch_multiplier = (11 - model_memory_gb) / 4 # 4GB per batch unit
recommended_batch = int(batch_multiplier * 32)
print(f"Recommended batch size for GTX 1080 Ti: {recommended_batch}")| Task | Performance | Comparison |
|---|---|---|
| ResNet-50 Training FP32 (imgs/sec) | 280 | FP32 only, no Tensor Cores |
| cuBLAS SGEMM (TFLOPS) | 10.8 | 96% of theoretical peak |
| Memory Bandwidth (GB/s measured) | 455 | 94% of theoretical peak |
| Stable Diffusion | ~15 sec | Works but slow |
| Classical ML (sklearn GPU) | Good | Rapids/cuML work well |
| LLM Inference | Limited | Quantization helps |
| Use Case | Rating | Notes |
|---|---|---|
| Learning CUDA | Excellent | Great for learning fundamentals |
| Classical ML/Rapids | Good | cuML works well |
| Deep Learning Training | Poor | No Tensor Cores, very slow |
| FP32 Inference | Fair | Works for FP32 models |
| Budget Experimentation | Excellent | Very cheap used |
| HPC/Scientific | Fair | Good FP32/FP64 still |
For learning and experimentation, yes. For production DL, no. The lack of Tensor Cores makes modern mixed-precision training very slow. Good for classical ML and CUDA learning.
Yes, but slowly. Expect 12-15 seconds for 512x512. The 11GB VRAM helps, but no FP16 acceleration. Consider for casual use only.
RTX 3060 is much better for ML due to Tensor Cores. Even with less raw FP32, the 3060 is 3-5x faster for DL workloads. Only choose 1080 Ti if it is essentially free.
CUDA 12.x supports Pascal (CC 6.1). However, some frameworks may drop support. CUDA 11.8 is safe. Check framework requirements before setup.
Tensor Cores, 12GB, much better for DL
Tensor Cores, 11GB, better for DL
Modern features, faster
Current gen entry, much better
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