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The NVIDIA GeForce RTX 3060 Ti delivers excellent value with 4,864 CUDA cores and 8GB GDDR6 memory. As a budget-focused Ampere card, it provides 3rd generation Tensor Cores and solid compute performance at an accessible price point, especially in the used market. For CUDA developers on a budget, the RTX 3060 Ti offers TF32 and mixed precision training capabilities at 200W TDP. The 8GB VRAM limits large models, but the card excels at inference, learning, and smaller training workloads with strong efficiency. This guide covers the RTX 3060 Ti's specifications, budget-conscious optimization strategies, and realistic performance expectations for CUDA development.
| Architecture | Ampere (GA104) |
| CUDA Cores | 4,864 |
| Tensor Cores | 152 |
| Memory | 8GB GDDR6 |
| Memory Bandwidth | 448 GB/s |
| Base / Boost Clock | 1410 / 1665 MHz |
| FP32 Performance | 16.2 TFLOPS |
| FP16 Performance | 32.4 TFLOPS |
| L2 Cache | 4MB |
| TDP | 200W |
| NVLink | No |
| MSRP | $399 |
| Release | December 2020 |
This code snippet shows how to detect your RTX 3060 Ti, check available memory, and configure optimal settings for the Ampere (GA104) architecture.
import torch
import pynvml
# Check if RTX 3060 Ti 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 Ti Memory: 8GB - Optimal batch sizes
# Architecture: Ampere (GA104)
# CUDA Cores: 4,864
# Memory-efficient training for RTX 3060 Ti
torch.backends.cuda.matmul.allow_tf32 = True # Enable TF32 for Ampere (GA104)
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 3060 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 RTX 3060 Ti: {recommended_batch}")| Task | Performance | Comparison |
|---|---|---|
| ResNet-50 Training (imgs/sec) | 620 | Solid budget training performance |
| BERT-Base Inference (sentences/sec) | 1,150 | Good for inference |
| Stable Diffusion (512x512, sec/img) | 7.8 | Usable for generation |
| LLaMA-7B Inference (tokens/sec) | 24 | Works with int8 quantization |
| cuBLAS SGEMM 4096x4096 (TFLOPS) | 15.3 | 94% of theoretical peak |
| Memory Bandwidth (GB/s measured) | 421 | 94% of theoretical peak |
| Use Case | Rating | Notes |
|---|---|---|
| Learning CUDA | Excellent | Great entry point with modern features |
| Small Model Training | Good | 8GB handles models up to 1-2B parameters |
| ML Inference | Good | Solid for FP16 inference workloads |
| Development & Prototyping | Good | Adequate for dev work |
| Large Model Training | Poor | 8GB too limiting |
| Budget Production | Fair | Works for cost-sensitive deployments |
Excellent choice. It has modern Tensor Cores, adequate VRAM for learning, and great used prices. You will learn all relevant CUDA features except FP8 operations which require Ada cards.
RTX 3060 Ti is faster, but 3060 has 12GB vs 8GB. For ML, the 3060 12GB is often better due to memory capacity. Consider your workload - if models fit in 8GB, get the faster 3060 Ti.
Yes, but limited to smaller models (under 2B parameters) with mixed precision and gradient checkpointing. Excellent for learning and experimenting, but limited for production-scale training.
In 2025, $200-250 used is excellent value. At that price, it is one of the best budget options for CUDA learning and small-scale ML work. Avoid paying over $280 used.
Slower but 12GB VRAM, better for ML
Similar performance, FP8, modern features
Faster, same 8GB VRAM
Much cheaper used, older features
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