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The NVIDIA GeForce RTX 3070 Ti delivers solid mid-range performance with 6,144 CUDA cores and 8GB GDDR6X memory. As an Ampere architecture card, it provides 3rd generation Tensor Cores and good compute efficiency for CUDA workloads at this performance tier. For CUDA developers, the RTX 3070 Ti offers a balance of performance and efficiency with 290W TDP. The 8GB VRAM limits large model training but the card handles inference, prototyping, and smaller training workloads effectively with TF32 and mixed precision support. This guide covers the RTX 3070 Ti's specifications, optimization strategies for working within memory constraints, and practical benchmarks for CUDA development.
| Architecture | Ampere (GA104) |
| CUDA Cores | 6,144 |
| Tensor Cores | 192 |
| Memory | 8GB GDDR6X |
| Memory Bandwidth | 608 GB/s |
| Base / Boost Clock | 1575 / 1770 MHz |
| FP32 Performance | 21.8 TFLOPS |
| FP16 Performance | 43.5 TFLOPS |
| L2 Cache | 4MB |
| TDP | 290W |
| NVLink | No |
| MSRP | $599 |
| Release | June 2021 |
This code snippet shows how to detect your RTX 3070 Ti, check available memory, and configure optimal settings for the Ampere (GA104) architecture.
import torch
import pynvml
# Check if RTX 3070 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 3070 Ti Memory: 8GB - Optimal batch sizes
# Architecture: Ampere (GA104)
# CUDA Cores: 6,144
# Memory-efficient training for RTX 3070 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 3070 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 3070 Ti: {recommended_batch}")| Task | Performance | Comparison |
|---|---|---|
| ResNet-50 Training (imgs/sec) | 780 | Good for mid-range card |
| BERT-Base Inference (sentences/sec) | 1,450 | Adequate inference performance |
| Stable Diffusion (512x512, sec/img) | 6.5 | Usable for generation |
| LLaMA-7B Inference (tokens/sec) | 32 | Works with quantization |
| cuBLAS SGEMM 4096x4096 (TFLOPS) | 20.5 | 94% of theoretical peak |
| Memory Bandwidth (GB/s measured) | 571 | 94% of theoretical peak |
| Use Case | Rating | Notes |
|---|---|---|
| Small Model Training | Good | 8GB limits to models under 2B parameters |
| ML Inference | Good | Solid for FP16 inference workloads |
| Development & Learning | Good | Adequate for CUDA development |
| Video Processing | Good | NVENC, VRAM limits complex projects |
| Large Model Training | Poor | 8GB too limiting |
| Scientific Computing | Fair | Good FP32, VRAM constrains datasets |
For smaller models and inference, yes. For training, you are limited to models under 2B parameters with optimization. The RTX 3070 Ti is best for learning, prototyping, and inference rather than large-scale training.
RTX 4060 Ti has FP8 support and larger L2 cache, making it better for inference. Both have 8GB VRAM. For new purchases, get the 4060 Ti unless the 3070 Ti is significantly cheaper used.
Yes, standard SD works well. 8GB handles SD 1.5 comfortably at 512x512. SDXL is possible but requires optimization and may be slow. Consider cards with more VRAM for SDXL work.
Only at significant discount vs newer cards. New, the RTX 4060 Ti or RTX 4070 offer better features and efficiency. Used at under $350, it becomes interesting for budget CUDA work.
Similar performance, FP8, better features
Slightly slower, same VRAM, less power
More affordable, slightly slower
Much better, 12GB VRAM, modern features
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