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The NVIDIA GeForce RTX 2070 Super offers entry-level RTX features with 2,560 CUDA cores and 8GB GDDR6 memory. As an older Turing card, it provides basic Tensor Core functionality at budget used prices, though it lacks the modern features that make newer cards effective for machine learning. For extreme budget CUDA work, the RTX 2070 Super represents one of the cheapest ways to access Tensor Cores, albeit first-generation ones. While not recommended for serious ML workloads, it can serve as a learning platform or handle light inference tasks at very low used prices. This guide sets realistic expectations for the RTX 2070 Super in 2025 and covers use cases where it might still be appropriate.
| Architecture | Turing (TU104) |
| CUDA Cores | 2,560 |
| Tensor Cores | 320 |
| Memory | 8GB GDDR6 |
| Memory Bandwidth | 448 GB/s |
| Base / Boost Clock | 1605 / 1770 MHz |
| FP32 Performance | 9.1 TFLOPS |
| FP16 Performance | 18.1 TFLOPS |
| L2 Cache | 4MB |
| TDP | 215W |
| NVLink | No |
| MSRP | $499 |
| Release | July 2019 |
This code snippet shows how to detect your RTX 2070 Super, check available memory, and configure optimal settings for the Turing (TU104) architecture.
import torch
import pynvml
# Check if RTX 2070 Super 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 Super Memory: 8GB - Optimal batch sizes
# Architecture: Turing (TU104)
# CUDA Cores: 2,560
# Memory-efficient training for RTX 2070 Super
torch.backends.cuda.matmul.allow_tf32 = True # Enable TF32 for Turing (TU104)
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 Super
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 Super: {recommended_batch}")| Task | Performance | Comparison |
|---|---|---|
| ResNet-50 Training FP16 (imgs/sec) | 310 | Very slow vs modern cards |
| BERT-Base Inference FP16 (sentences/sec) | 520 | Barely adequate |
| Stable Diffusion (512x512, sec/img) | 15.2 | Quite slow |
| cuBLAS SGEMM 4096x4096 (TFLOPS) | 8.6 | 94% of theoretical peak |
| Memory Bandwidth (GB/s measured) | 421 | 94% of theoretical peak |
| Classical CUDA workloads | Decent | Good FP32 performance |
| Use Case | Rating | Notes |
|---|---|---|
| Learning Basic CUDA | Fair | Works but outdated architecture |
| Classical CUDA Algorithms | Fair | Acceptable FP32 compute |
| Light Inference | Poor | Slow even for inference |
| ML Training | Poor | Not recommended |
| Extreme Budget | Fair | Cheapest RTX option ($150-200 used) |
| Modern Workflows | Poor | Too slow for current ML |
Only if under $180 used and only for basic CUDA learning. For any ML work, save up for RTX 3060 or newer. The 2070 Super lacks TF32 and has very slow Tensor Cores.
Yes, but very slowly (15+ seconds per image). If SD is your goal, this is not a good choice. Even RTX 3060 Ti is 2x faster and available under $250 used.
Barely acceptable for learning basic CUDA programming. For learning modern ML, the lack of TF32 means you miss important current workflows. Stretch budget for RTX 3060 if possible.
Classical CUDA algorithms, basic GPU programming learning, light inference with quantized models, and general compute. Not suitable for serious ML training or modern deep learning workflows.
Much better for ML, 12GB, worth the extra cost
Significantly faster, TF32 support
Cheaper, similar limitations
No Tensor Cores but very cheap
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