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The NVIDIA GeForce GTX 1080 was a powerful Pascal GPU in its time, but lacks the Tensor Cores that define modern ML acceleration. With 2,560 CUDA cores and 8GB GDDR5X, it provides basic CUDA compute but is significantly limited for machine learning workloads. For CUDA developers with a GTX 1080, the absence of Tensor Cores means no hardware FP16 acceleration. Training and inference are much slower than RTX-series GPUs. It remains useful for learning CUDA basics but is not recommended for ML work. This guide covers the GTX 1080's limitations, what it can still do, and upgrade recommendations.
| Architecture | Pascal (GP104) |
| CUDA Cores | 2,560 |
| Tensor Cores | 0 |
| Memory | 8GB GDDR5X |
| Memory Bandwidth | 320 GB/s |
| Base / Boost Clock | 1607 / 1733 MHz |
| FP32 Performance | 8.9 TFLOPS |
| FP16 Performance | 0.17 TFLOPS |
| L2 Cache | 2MB |
| TDP | 180W |
| NVLink | No |
| MSRP | $599 |
| Release | May 2016 |
This code snippet shows how to detect your GTX 1080, check available memory, and configure optimal settings for the Pascal (GP104) architecture.
import torch
import pynvml
# Check if GTX 1080 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 Memory: 8GB - Optimal batch sizes
# Architecture: Pascal (GP104)
# CUDA Cores: 2,560
# Memory-efficient training for GTX 1080
torch.backends.cuda.matmul.allow_tf32 = True # Enable TF32 for Pascal (GP104)
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 / 8 GB total")
# Recommended batch size calculation for GTX 1080
model_memory_gb = 2.0 # Adjust based on your model
batch_multiplier = (8 - model_memory_gb) / 4 # 4GB per batch unit
recommended_batch = int(batch_multiplier * 32)
print(f"Recommended batch size for GTX 1080: {recommended_batch}")| Task | Performance | Comparison |
|---|---|---|
| ResNet-50 Training (imgs/sec) | 90 | FP32 only, very slow |
| BERT Inference (sentences/sec) | 80 | No Tensor Cores |
| Stable Diffusion (512x512, sec/img) | 45 | Impractically slow |
| cuBLAS SGEMM 4096x4096 (TFLOPS) | 8.5 | 96% efficiency |
| Memory Bandwidth (GB/s measured) | 300 | 94% efficiency |
| FP16 Performance | 0.17 TFLOPS | Essentially no FP16 |
| Use Case | Rating | Notes |
|---|---|---|
| Learning CUDA Basics | Fair | Can learn fundamentals |
| ML Training | Poor | No Tensor Cores, very slow |
| ML Inference | Poor | No acceleration |
| Gaming/Graphics | Good | Original purpose |
| Scientific Computing | Fair | Basic FP32/FP64 |
| Production ML | Poor | Not viable |
Technically yes, but it is 5-10x slower than RTX cards due to no Tensor Cores. Not practical for any serious ML work in 2025. Use it only for learning CUDA basics.
Absolutely, if you do any ML work. Even an RTX 3050 with Tensor Cores is significantly better for ML. RTX 3060 12GB is the recommended minimum.
Pascal GPUs lack Tensor Cores which provide 8-16x acceleration for ML operations. GTX 1080 must use slow FP32 paths for operations that RTX cards accelerate in hardware.
Gaming and basic CUDA learning. For compute workloads, its only role is teaching CUDA fundamentals before moving to RTX hardware for actual ML work.
Tensor Cores, much better for ML
12GB, vastly superior for ML
11GB, still no Tensor Cores
Tensor Cores, 6GB
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