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The NVIDIA GeForce RTX 4060 brings Ada Lovelace architecture to the mainstream market, offering 3,072 CUDA cores and 8GB GDDR6 memory at an accessible price point. As an entry-level RTX 40 series card, it provides modern features including 4th generation Tensor Cores and DLSS 3 support. For CUDA developers on a budget, the RTX 4060 delivers excellent efficiency with only 115W TDP while supporting FP8 precision for inference workloads. The 8GB VRAM limits large model training but handles inference, prototyping, and smaller models effectively. This guide covers the RTX 4060's specifications, CUDA optimization strategies, benchmark results, and practical tips for maximizing performance in resource-constrained environments.
| Architecture | Ada Lovelace (AD107) |
| CUDA Cores | 3,072 |
| Tensor Cores | 96 |
| Memory | 8GB GDDR6 |
| Memory Bandwidth | 272 GB/s |
| Base / Boost Clock | 1830 / 2460 MHz |
| FP32 Performance | 15.1 TFLOPS |
| FP16 Performance | 30.2 TFLOPS |
| L2 Cache | 24MB |
| TDP | 115W |
| NVLink | No |
| MSRP | $299 |
| Release | June 2023 |
This code snippet shows how to detect your RTX 4060, check available memory, and configure optimal settings for the Ada Lovelace (AD107) architecture.
import torch
import pynvml
# Check if RTX 4060 is available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Using device: {torch.cuda.get_device_name(0)}")
# RTX 4060 Memory: 8GB - Optimal batch sizes
# Architecture: Ada Lovelace (AD107)
# CUDA Cores: 3,072
# Memory-efficient training for RTX 4060
torch.backends.cuda.matmul.allow_tf32 = True # Enable TF32 for Ada Lovelace (AD107)
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 4060
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 4060: {recommended_batch}")| Task | Performance | Comparison |
|---|---|---|
| ResNet-50 Training (imgs/sec) | 420 | Good for entry-level training |
| BERT-Base Inference (sentences/sec) | 1,850 | Excellent for inference |
| Stable Diffusion (512x512, sec/img) | 8.5 | Usable for casual generation |
| LLaMA-7B Inference (tokens/sec) | 25 | Works with quantization |
| cuBLAS SGEMM 4096x4096 (TFLOPS) | 14.2 | 94% of theoretical peak |
| Memory Bandwidth (GB/s measured) | 255 | 94% of theoretical peak |
| Use Case | Rating | Notes |
|---|---|---|
| ML Inference | Excellent | FP8 support makes it great for deployment |
| Learning & Development | Excellent | Perfect entry point for CUDA development |
| Small Model Training | Good | 8GB handles models up to ~1B parameters |
| Video Processing | Good | AV1 encode, limited by VRAM |
| Large Model Training | Poor | 8GB is too limiting |
| Scientific Computing | Fair | Good FP32 but VRAM limits dataset size |
For inference and small models, yes. For training, you are limited to models under 1B parameters with quantization. The RTX 4060 is best for learning, prototyping, and inference rather than serious training.
RTX 4060 is faster per CUDA core and has FP8 support, but RTX 3060 12GB has 50% more VRAM. For ML, the 3060 12GB is often better due to memory. For inference and general CUDA, the 4060 is more efficient.
Yes, with quantization. You can run quantized 7B models (4-bit) for inference. Training requires extreme optimization or is impractical. Use this card for inference and experimentation only.
The 115W TDP is very modest. A 450W PSU is sufficient for most systems. This makes the RTX 4060 ideal for compact builds and systems with limited power budgets.
35% faster, same VRAM, $100 more
Slower but 12GB VRAM, better for ML
2x faster with 12GB, significant upgrade
Professional option with 16GB
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