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The NVIDIA GeForce RTX 3060 offers an interesting value proposition: 12GB of VRAM at entry-level pricing. With 3,584 CUDA cores, it has lower compute than the RTX 3070, but the extra VRAM makes it surprisingly capable for memory-hungry ML workloads. For CUDA developers on tight budgets, the RTX 3060's 12GB VRAM enables running models that would not fit on the RTX 3070's 8GB. This makes it a popular choice for LLM inference and Stable Diffusion work. This guide covers strategies for leveraging the RTX 3060's unique strengths.
| Architecture | Ampere (GA106) |
| CUDA Cores | 3,584 |
| Tensor Cores | 112 |
| Memory | 12GB GDDR6 |
| Memory Bandwidth | 360 GB/s |
| Base / Boost Clock | 1320 / 1777 MHz |
| FP32 Performance | 12.7 TFLOPS |
| FP16 Performance | 25.5 TFLOPS |
| L2 Cache | 3MB |
| TDP | 170W |
| NVLink | No |
| MSRP | $329 |
| Release | February 2021 |
This code snippet shows how to detect your RTX 3060, check available memory, and configure optimal settings for the Ampere (GA106) architecture.
import torch
import pynvml
# Check if RTX 3060 is available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Using device: {torch.cuda.get_device_name(0)}")
# RTX 3060 Memory: 12GB - Optimal batch sizes
# Architecture: Ampere (GA106)
# CUDA Cores: 3,584
# Memory-efficient training for RTX 3060
torch.backends.cuda.matmul.allow_tf32 = True # Enable TF32 for Ampere (GA106)
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 / 12 GB total")
# Recommended batch size calculation for RTX 3060
model_memory_gb = 2.0 # Adjust based on your model
batch_multiplier = (12 - model_memory_gb) / 4 # 4GB per batch unit
recommended_batch = int(batch_multiplier * 32)
print(f"Recommended batch size for RTX 3060: {recommended_batch}")| Task | Performance | Comparison |
|---|---|---|
| ResNet-50 Training (imgs/sec) | 420 | 60% of RTX 3070 |
| BERT-Base Inference (sentences/sec) | 680 | Good for inference |
| Stable Diffusion (512x512, sec/img) | 9.5 | 12GB helps with SDXL |
| LLaMA-7B Inference (tokens/sec) | 18 | 12GB fits 8-bit model |
| cuBLAS SGEMM 8192x8192 (TFLOPS) | 11.8 | 93% of theoretical peak |
| Memory Bandwidth (GB/s measured) | 340 | 94% of theoretical peak |
| Use Case | Rating | Notes |
|---|---|---|
| LLM Inference | Good | 12GB fits quantized 7B-13B models |
| Stable Diffusion | Good | 12GB enables SDXL |
| Deep Learning Training | Fair | Low compute limits training speed |
| Learning/Education | Excellent | Very affordable entry point |
| Hobbyist ML | Excellent | Best VRAM per dollar |
| Development | Good | Good for testing memory-heavy code |
RTX 3070 is 60% faster but has only 8GB. RTX 3060 12GB is better for VRAM-limited tasks like LLMs and SDXL. Choose 3070 for training speed, 3060 for VRAM-hungry inference.
Yes! The 12GB VRAM is its strength. 8-bit quantized 7B models fit comfortably. Even 13B with 4-bit quantization works. Slower than higher cards but fits models that wont run on 8GB cards.
Surprisingly good. The 12GB VRAM means SDXL works without issues. Generation is slower than 3070/3080 but the VRAM headroom is valuable. Popular choice for SD hobbyists.
RTX 3060 Ti is 30% faster but only 8GB. For pure training/gaming, 3060 Ti. For LLMs and large models, the 3060 12GB is actually better due to VRAM.
60% faster, 8GB VRAM
16GB, faster, newer
30% faster, 8GB
Same VRAM, 2x faster
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