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The NVIDIA GeForce RTX 4070 is the most affordable Ada Lovelace GPU, offering modern features like FP8 Tensor Cores at an accessible price. With 5,888 CUDA cores and 12GB GDDR6X, it provides a solid foundation for ML development and inference. For CUDA developers on a budget, the RTX 4070 brings 4th generation Tensor Cores to a mainstream price point. The 12GB VRAM handles most development workloads, and FP8 support enables efficient quantized inference. This guide covers making the most of the RTX 4070's capabilities for ML workloads.
| Architecture | Ada Lovelace (AD104) |
| CUDA Cores | 5,888 |
| Tensor Cores | 184 |
| Memory | 12GB GDDR6X |
| Memory Bandwidth | 504 GB/s |
| Base / Boost Clock | 1920 / 2475 MHz |
| FP32 Performance | 29.1 TFLOPS |
| FP16 Performance | 58.2 TFLOPS |
| L2 Cache | 36MB |
| TDP | 200W |
| NVLink | No |
| MSRP | $599 |
| Release | April 2023 |
This code snippet shows how to detect your RTX 4070, check available memory, and configure optimal settings for the Ada Lovelace (AD104) architecture.
import torch
import pynvml
# Check if RTX 4070 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 Memory: 12GB - Optimal batch sizes
# Architecture: Ada Lovelace (AD104)
# CUDA Cores: 5,888
# Memory-efficient training for RTX 4070
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
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: {recommended_batch}")| Task | Performance | Comparison |
|---|---|---|
| ResNet-50 Training (imgs/sec) | 780 | 74% of RTX 4070 Ti |
| BERT-Large Inference (sentences/sec) | 1,350 | FP8 optimized |
| Stable Diffusion (512x512, sec/img) | 5.2 | Good SD performance |
| LLaMA-7B Inference (tokens/sec) | 42 | 8-bit quantized |
| cuBLAS SGEMM 8192x8192 (TFLOPS) | 27.5 | 94% of theoretical peak |
| Memory Bandwidth (GB/s measured) | 475 | 94% of theoretical peak |
| Use Case | Rating | Notes |
|---|---|---|
| ML Learning/Education | Excellent | Great for learning with modern features |
| Inference Development | Excellent | FP8 enables efficient inference testing |
| Small Model Training | Good | 12GB handles medium models |
| Stable Diffusion | Good | Handles SD well at 12GB |
| Budget ML Workstation | Excellent | Best value current-gen |
| LLM Inference | Good | Quantized 7B models run well |
Good for learning, development, and inference. Training is limited by 12GB VRAM to smaller models. The FP8 Tensor Cores make it excellent for inference testing.
Similar performance but RTX 4070 has FP8 Tensor Cores and larger L2 cache. RTX 4070 better for inference, RTX 3080 has slightly more raw compute. 4070 is more efficient.
Yes, with quantization. 12GB handles 8-bit 7B models well. For larger models, need 4-bit quantization. Good for LLM experimentation and inference.
RTX 4070 Ti is 30% faster for $200 more. If budget allows, 4070 Ti is better value. For casual ML work and learning, 4070 is sufficient.
30% faster, $200 more
Older gen, 10GB, similar perf
Older gen, 8GB, cheaper used
16GB variant, lower compute
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