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The NVIDIA GeForce RTX 2070 offered Tensor Core capabilities at a more accessible price point than the RTX 2080. With 2,304 CUDA cores and 8GB GDDR6, it provided a solid entry point for ML developers seeking hardware acceleration. For CUDA developers with an RTX 2070, the 8GB VRAM limitation is significant in 2025, but the 1st gen Tensor Cores still provide useful FP16 acceleration for smaller workloads and learning purposes. This guide covers the RTX 2070's specifications, realistic capabilities today, and optimization tips.
| Architecture | Turing (TU106) |
| CUDA Cores | 2,304 |
| Tensor Cores | 288 |
| Memory | 8GB GDDR6 |
| Memory Bandwidth | 448 GB/s |
| Base / Boost Clock | 1410 / 1620 MHz |
| FP32 Performance | 7.5 TFLOPS |
| FP16 Performance | 15 TFLOPS |
| L2 Cache | 4MB |
| TDP | 185W |
| NVLink | No |
| MSRP | $599 |
| Release | October 2018 |
This code snippet shows how to detect your RTX 2070, check available memory, and configure optimal settings for the Turing (TU106) architecture.
import torch
import pynvml
# Check if RTX 2070 is available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Using device: {torch.cuda.get_device_name(0)}")
# RTX 2070 Memory: 8GB - Optimal batch sizes
# Architecture: Turing (TU106)
# CUDA Cores: 2,304
# Memory-efficient training for RTX 2070
torch.backends.cuda.matmul.allow_tf32 = True # Enable TF32 for Turing (TU106)
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 RTX 2070
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 RTX 2070: {recommended_batch}")| Task | Performance | Comparison |
|---|---|---|
| ResNet-50 Training (imgs/sec) | 280 | FP16 mixed precision |
| BERT-Base Inference (sentences/sec) | 300 | FP16 mode |
| Stable Diffusion (512x512, sec/img) | 20 | Very slow, limited |
| Small Model Training | Adequate | For learning |
| cuBLAS SGEMM 4096x4096 (TFLOPS) | 7.2 | 96% efficiency |
| Memory Bandwidth (GB/s measured) | 420 | 94% efficiency |
| Use Case | Rating | Notes |
|---|---|---|
| Learning CUDA/ML | Good | Affordable Tensor Core entry |
| Small Model Training | Fair | 8GB limits model size |
| Basic Inference | Fair | Works for small models |
| Production ML | Poor | Too limited in 2025 |
| Large Models | Poor | 8GB insufficient |
| Prototyping | Fair | Before scaling up |
If you already own one, it is fine for learning and small experiments. Not worth buying now - RTX 3060 12GB is much better for similar used prices.
RTX 3060 has newer Tensor Cores with TF32 support and 12GB vs 8GB VRAM. The 3060 is significantly more capable for ML despite similar FP32 performance.
Yes, for small models and learning. The 8GB VRAM limits practical applications in 2025. Tensor Cores do accelerate training and inference for smaller workloads.
For ML work, yes. RTX 3060 12GB, RTX 4060, or higher are much more capable. The jump to 12-16GB VRAM and newer Tensor Cores is significant.
12GB, much better for ML
Ada, FP8, 8GB
Faster, same 8GB
Newer but similar limitations
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