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The NVIDIA GeForce GTX 1070 Ti was positioned between the GTX 1070 and GTX 1080, offering near-1080 performance at a lower price. However, like all Pascal GPUs, it lacks Tensor Cores making it unsuitable for modern ML workloads. For CUDA developers, the GTX 1070 Ti has no hardware ML acceleration. The 8GB GDDR5 is slower than GDDR5X, and without Tensor Cores, training and inference are extremely slow compared to RTX cards. This guide covers what the GTX 1070 Ti can and cannot do for CUDA development in 2025.
| Architecture | Pascal (GP104) |
| CUDA Cores | 2,432 |
| Tensor Cores | 0 |
| Memory | 8GB GDDR5 |
| Memory Bandwidth | 256 GB/s |
| Base / Boost Clock | 1607 / 1683 MHz |
| FP32 Performance | 8.2 TFLOPS |
| FP16 Performance | 0.16 TFLOPS |
| L2 Cache | 2MB |
| TDP | 180W |
| NVLink | No |
| MSRP | $449 |
| Release | November 2017 |
This code snippet shows how to detect your GTX 1070 Ti, check available memory, and configure optimal settings for the Pascal (GP104) architecture.
import torch
import pynvml
# Check if GTX 1070 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 1070 Ti Memory: 8GB - Optimal batch sizes
# Architecture: Pascal (GP104)
# CUDA Cores: 2,432
# Memory-efficient training for GTX 1070 Ti
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 Ti
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 Ti: {recommended_batch}")| Task | Performance | Comparison |
|---|---|---|
| ResNet-50 Training (imgs/sec) | 75 | FP32 only, impractical |
| BERT Inference (sentences/sec) | 65 | No acceleration |
| Stable Diffusion | Not recommended | Far too slow |
| cuBLAS SGEMM 4096x4096 (TFLOPS) | 7.8 | 95% efficiency |
| Memory Bandwidth (GB/s measured) | 240 | 94% efficiency |
| FP16 Performance | 0.16 TFLOPS | Essentially none |
| Use Case | Rating | Notes |
|---|---|---|
| Learning Basic CUDA | Fair | Fundamentals only |
| ML Training | Poor | No Tensor Cores |
| ML Inference | Poor | No acceleration |
| Gaming | Fair | Original purpose, dated |
| Scientific FP32 | Fair | Basic compute |
| Production | Poor | Not viable |
Technically possible but not practical. Without Tensor Cores, it is 5-10x slower than entry RTX cards. Not recommended for any ML work.
No, never. Even free, you should spend on an RTX 3050 instead. The lack of Tensor Cores makes it useless for practical ML in 2025.
Basic CUDA programming education and legacy gaming. For any compute workload, especially ML, it is obsolete. Upgrade to any RTX card.
RTX 3050 is dramatically better despite lower FP32 TFLOPS. Tensor Cores provide 5-10x speedup for ML operations. RTX 3050 is the correct choice.
Much better with Tensor Cores
12GB, vastly superior
Slightly faster, same limitations
Tensor Cores, proper ML GPU
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