Loading...
The NVIDIA GeForce GTX 1070 from the Pascal generation offers 1,920 CUDA cores and 8GB GDDR5 memory. As a legacy card from 2016, it lacks Tensor Cores and modern features, making it relevant only for extreme budget scenarios or classical CUDA workloads. For minimal budget CUDA learning, the GTX 1070 provides 8GB VRAM at very low used prices. However, without Tensor Cores and with slow FP16 performance, it is unsuitable for modern ML. Consider it only for learning basic CUDA concepts or classical algorithms. This guide provides realistic expectations for using the GTX 1070 in 2025 as a legacy learning platform.
| Architecture | Pascal (GP104) |
| CUDA Cores | 1,920 |
| Tensor Cores | 0 |
| Memory | 8GB GDDR5 |
| Memory Bandwidth | 256 GB/s |
| Base / Boost Clock | 1506 / 1683 MHz |
| FP32 Performance | 6.5 TFLOPS |
| FP16 Performance | 0.2 TFLOPS |
| L2 Cache | 2MB |
| TDP | 150W |
| NVLink | No |
| MSRP | $379 |
| Release | June 2016 |
This code snippet shows how to detect your GTX 1070, check available memory, and configure optimal settings for the Pascal (GP104) architecture.
import torch
import pynvml
# Check if GTX 1070 is available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Using device: {torch.cuda.get_device_name(0)}")
# GTX 1070 Memory: 8GB - Optimal batch sizes
# Architecture: Pascal (GP104)
# CUDA Cores: 1,920
# Memory-efficient training for GTX 1070
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 1070
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 1070: {recommended_batch}")| Task | Performance | Comparison |
|---|---|---|
| ResNet-50 Training FP32 (imgs/sec) | 85 | Very slow, FP32 only |
| cuBLAS SGEMM 2048x2048 (TFLOPS) | 6.1 | 94% of theoretical peak |
| Memory Bandwidth (GB/s measured) | 240 | 94% of theoretical peak |
| Classical CUDA | Fair | Adequate FP32 |
| Any DL training | Poor | No Tensor Cores |
| Modern ML | Unusable | Too old and slow |
| Use Case | Rating | Notes |
|---|---|---|
| Learning CUDA Basics | Poor | Very old architecture |
| Classical Algorithms | Fair | FP32 compute adequate |
| ML Training | Poor | Not viable |
| ML Inference | Poor | Too slow |
| Extreme Budget | Fair | Ultra-cheap ($80-100 used) |
| Any Modern Work | Poor | Too outdated |
Not recommended. No Tensor Cores means extremely slow training. Only consider if you have literally no other option and need 8GB VRAM at rock-bottom price for basic learning.
You can learn basic CUDA syntax and concepts, but miss all modern features (Tensor Cores, TF32, FP8, etc). For $150-200 more, RTX 3060 Ti provides proper modern learning experience.
Classical CUDA algorithms, basic GPU programming learning (syntax only), general compute tasks. Not suitable for ML, DL, or any production work.
Save for newer card. The GTX 1070 only makes sense if your total budget is under $100 and you only need to learn absolute basic CUDA concepts.
Vastly better, Tensor Cores, 12GB
Slightly newer, similar limitations
Tensor Cores, worth the extra cost
11GB VRAM, better Pascal option
Ready to optimize your CUDA kernels for GTX 1070? Download RightNow AI for real-time performance analysis.