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The NVIDIA GeForce RTX 4070 Ti brings Ada Lovelace architecture to a more accessible price point. With 7,680 CUDA cores and 12GB GDDR6X memory, it offers modern features like FP8 Tensor Cores while keeping costs reasonable. For CUDA developers on a budget, the RTX 4070 Ti provides 4th generation Tensor Cores with FP8 support, making it excellent for inference workloads. The 12GB VRAM handles most development tasks, though large model training requires careful memory management. This guide covers optimization strategies specific to the RTX 4070 Ti's architecture and memory constraints.
| Architecture | Ada Lovelace (AD104) |
| CUDA Cores | 7,680 |
| Tensor Cores | 240 |
| Memory | 12GB GDDR6X |
| Memory Bandwidth | 504 GB/s |
| Base / Boost Clock | 2310 / 2610 MHz |
| FP32 Performance | 40.1 TFLOPS |
| FP16 Performance | 80.2 TFLOPS |
| L2 Cache | 48MB |
| TDP | 285W |
| NVLink | No |
| MSRP | $799 |
| Release | January 2023 |
This code snippet shows how to detect your RTX 4070 Ti, check available memory, and configure optimal settings for the Ada Lovelace (AD104) architecture.
import torch
import pynvml
# Check if RTX 4070 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 4070 Ti Memory: 12GB - Optimal batch sizes
# Architecture: Ada Lovelace (AD104)
# CUDA Cores: 7,680
# Memory-efficient training for RTX 4070 Ti
torch.backends.cuda.matmul.allow_tf32 = True # Enable TF32 for Ada Lovelace (AD104)
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 4070 Ti
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 4070 Ti: {recommended_batch}")| Task | Performance | Comparison |
|---|---|---|
| ResNet-50 Training (imgs/sec) | 1,050 | 80% of RTX 4080 |
| BERT-Large Inference (sentences/sec) | 1,780 | FP8 boosts inference |
| Stable Diffusion (512x512, sec/img) | 4.2 | Fast SD generation |
| LLaMA-7B Inference (tokens/sec) | 52 | 8-bit quantized |
| cuBLAS SGEMM 8192x8192 (TFLOPS) | 38.1 | 95% of theoretical peak |
| Memory Bandwidth (GB/s measured) | 475 | 94% of theoretical peak |
| Use Case | Rating | Notes |
|---|---|---|
| ML Inference | Excellent | FP8 Tensor Cores excel at inference |
| Deep Learning Training | Good | 12GB handles medium models well |
| Development/Prototyping | Excellent | Modern features at good price |
| Stable Diffusion | Excellent | Fast generation, handles SDXL |
| Video AI Processing | Excellent | Dual NVENC with AV1 |
| Budget ML Workstation | Excellent | Best value current-gen |
Excellent for development. The FP8 support and large L2 cache make it great for prototyping and inference. 12GB VRAM handles most development workloads.
RTX 4070 Ti is about 35% faster with better Tensor Cores (FP8) and larger L2 cache. RTX 3080 has slightly higher memory bandwidth. For new purchases, 4070 Ti is better value.
Yes, with quantization. 12GB handles 7B models with 8-bit or 13B with 4-bit quantization well. The FP8 Tensor Cores boost quantized inference performance.
RTX 4080 offers 25% more performance and 16GB VRAM for $400 more. If you need the extra VRAM for larger models, yes. For inference and smaller training, 4070 Ti is great value.
25% faster, 16GB, $400 more
Older gen, 10GB, lower price used
25% slower, 12GB, $200 less
24GB VRAM, similar perf, good used
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