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The NVIDIA GeForce RTX 4060 Ti 8GB delivers enhanced performance over the RTX 4060 with 4,352 CUDA cores while maintaining the same 8GB VRAM capacity. Built on Ada Lovelace architecture, it offers excellent efficiency at 160W TDP with modern features including FP8 Tensor Cores. For CUDA developers, the RTX 4060 Ti 8GB provides approximately 35% more compute than the RTX 4060 at a modest price premium. The larger 32MB L2 cache improves memory-bound kernel performance, though the 8GB VRAM remains a limitation for large model training. This guide covers the RTX 4060 Ti 8GB's specifications, optimization strategies for working within VRAM constraints, and benchmark results for CUDA workloads.
| Architecture | Ada Lovelace (AD106) |
| CUDA Cores | 4,352 |
| Tensor Cores | 136 |
| Memory | 8GB GDDR6 |
| Memory Bandwidth | 288 GB/s |
| Base / Boost Clock | 2310 / 2535 MHz |
| FP32 Performance | 22.1 TFLOPS |
| FP16 Performance | 44.1 TFLOPS |
| L2 Cache | 32MB |
| TDP | 160W |
| NVLink | No |
| MSRP | $399 |
| Release | May 2023 |
This code snippet shows how to detect your RTX 4060 Ti 8GB, check available memory, and configure optimal settings for the Ada Lovelace (AD106) architecture.
import torch
import pynvml
# Check if RTX 4060 Ti 8GB 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 Ti 8GB Memory: 8GB - Optimal batch sizes
# Architecture: Ada Lovelace (AD106)
# CUDA Cores: 4,352
# Memory-efficient training for RTX 4060 Ti 8GB
torch.backends.cuda.matmul.allow_tf32 = True # Enable TF32 for Ada Lovelace (AD106)
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 Ti 8GB
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 Ti 8GB: {recommended_batch}")| Task | Performance | Comparison |
|---|---|---|
| ResNet-50 Training (imgs/sec) | 580 | 38% faster than RTX 4060 |
| BERT-Base Inference (sentences/sec) | 2,450 | Excellent inference performance |
| Stable Diffusion (512x512, sec/img) | 6.2 | Good for creative workflows |
| LLaMA-7B Inference (tokens/sec) | 34 | Works with quantization |
| cuBLAS SGEMM 4096x4096 (TFLOPS) | 20.8 | 94% of theoretical peak |
| Memory Bandwidth (GB/s measured) | 270 | 94% of theoretical peak |
| Use Case | Rating | Notes |
|---|---|---|
| ML Inference | Excellent | FP8 Tensor Cores deliver strong inference |
| Small Model Training | Good | 8GB handles models up to 1-2B parameters |
| Development & Prototyping | Excellent | Good balance of performance and cost |
| Video Processing | Good | AV1 encoding, VRAM limits complex projects |
| Large Model Training | Poor | 8GB too limiting for modern LLMs |
| Scientific Computing | Good | Strong FP32, VRAM limits large datasets |
For ML work, get the 16GB variant if possible. The 8GB version is fine for inference and small models, but the 16GB unlocks significantly larger models. The performance is identical, only VRAM differs.
Approximately 35-40% faster in most workloads. The extra CUDA cores and larger L2 cache provide consistent improvements. However, both share the 8GB VRAM limitation in this variant.
Yes, but with significant limitations. You are restricted to small models (under 2B parameters) with mixed precision and gradient checkpointing. Consider the 16GB variant or RTX 4070 for serious training.
Excellent for inference. The FP8 Tensor Cores and efficient architecture make it ideal for serving quantized models. 8GB handles most inference workloads well, especially with int8 quantization.
Same performance, 2x VRAM, $100 more
35% slower but $100 less
50% faster with 12GB, better value
Slower but 12GB VRAM
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