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The NVIDIA GeForce RTX 2060 represents the entry point to RTX features with 1,920 CUDA cores and 6GB GDDR6 memory. As the most affordable Turing card, it provides basic Tensor Core access at very low used prices, though the limited VRAM and dated architecture restrict its usefulness for modern CUDA workloads. For extreme budget scenarios, the RTX 2060 offers the absolute minimum to experiment with Tensor Cores and ray tracing. However, the 6GB VRAM is severely limiting for 2025 ML workloads, and the lack of TF32 makes training very slow compared to even entry-level Ampere cards. This guide provides realistic expectations for the RTX 2060 and identifies the narrow use cases where it might still be appropriate.
| Architecture | Turing (TU106) |
| CUDA Cores | 1,920 |
| Tensor Cores | 240 |
| Memory | 6GB GDDR6 |
| Memory Bandwidth | 336 GB/s |
| Base / Boost Clock | 1365 / 1680 MHz |
| FP32 Performance | 6.5 TFLOPS |
| FP16 Performance | 13 TFLOPS |
| L2 Cache | 3MB |
| TDP | 160W |
| NVLink | No |
| MSRP | $349 |
| Release | January 2019 |
This code snippet shows how to detect your RTX 2060, check available memory, and configure optimal settings for the Turing (TU106) architecture.
import torch
import pynvml
# Check if RTX 2060 is available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Using device: {torch.cuda.get_device_name(0)}")
# RTX 2060 Memory: 6GB - Optimal batch sizes
# Architecture: Turing (TU106)
# CUDA Cores: 1,920
# Memory-efficient training for RTX 2060
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 / 6 GB total")
# Recommended batch size calculation for RTX 2060
model_memory_gb = 2.0 # Adjust based on your model
batch_multiplier = (6 - model_memory_gb) / 4 # 4GB per batch unit
recommended_batch = int(batch_multiplier * 32)
print(f"Recommended batch size for RTX 2060: {recommended_batch}")| Task | Performance | Comparison |
|---|---|---|
| ResNet-50 Training FP16 (imgs/sec) | 185 | Very slow, small batches only |
| BERT-Base Inference FP16 (sentences/sec) | 310 | Barely usable |
| Stable Diffusion (512x512, sec/img) | 18-20 | Painfully slow |
| cuBLAS SGEMM 2048x2048 (TFLOPS) | 6.1 | 94% of theoretical peak |
| Memory Bandwidth (GB/s measured) | 316 | 94% of theoretical peak |
| Modern ML workloads | Poor | 6GB too limiting |
| Use Case | Rating | Notes |
|---|---|---|
| Learning Basic CUDA | Poor | 6GB very limiting even for learning |
| Classical CUDA | Fair | Acceptable for simple algorithms |
| ML Inference | Poor | 6GB too small for most models |
| ML Training | Poor | Not recommended |
| Absolute Minimum Budget | Fair | Cheapest RTX option ($120-150) |
| Any Modern Workload | Poor | Insufficient for 2025 needs |
No. 6GB is too limiting for modern ML workloads. Even Stable Diffusion struggles. If this is your only option, it works for absolute basic learning, but save up for RTX 3060 12GB instead.
Only if under $130 and you have no other option. For $200-250, RTX 3060 Ti is vastly better. The 6GB VRAM will frustrate you even when learning due to constant OOM errors.
Only very small models or with aggressive quantization. Most modern workflows assume 8GB minimum. The RTX 2060 is too limited for practical ML work in 2025.
Gaming on a budget, basic CUDA learning (very basic), and light classical compute tasks. Not suitable for machine learning, modern deep learning, or any memory-intensive CUDA work.
Much better, 12GB, worth saving for
Far superior for ML work
No Tensor Cores but cheaper and similar usefulness
Slightly better, 8GB VRAM
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