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The NVIDIA GeForce RTX 4070 Super delivers a meaningful upgrade over the original RTX 4070 with 20% more CUDA cores while maintaining the same $599 price point. Built on Ada Lovelace with 7,168 CUDA cores and 12GB GDDR6X, it offers excellent performance per dollar. For CUDA developers, the RTX 4070 Super provides strong FP8 inference capabilities and good training performance for smaller models. The 12GB VRAM remains a consideration for memory-intensive workloads, but for many use cases it's sufficient. This guide covers the RTX 4070 Super's specifications, CUDA optimization strategies, and practical tips for maximizing performance.
| Architecture | Ada Lovelace (AD104) |
| CUDA Cores | 7,168 |
| Tensor Cores | 224 |
| Memory | 12GB GDDR6X |
| Memory Bandwidth | 504 GB/s |
| Base / Boost Clock | 1980 / 2475 MHz |
| FP32 Performance | 35.5 TFLOPS |
| FP16 Performance | 71 TFLOPS |
| L2 Cache | 48MB |
| TDP | 220W |
| NVLink | No |
| MSRP | $599 |
| Release | January 2024 |
This code snippet shows how to detect your RTX 4070 Super, check available memory, and configure optimal settings for the Ada Lovelace (AD104) architecture.
import torch
import pynvml
# Check if RTX 4070 Super 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 Super Memory: 12GB - Optimal batch sizes
# Architecture: Ada Lovelace (AD104)
# CUDA Cores: 7,168
# Memory-efficient training for RTX 4070 Super
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 Super
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 Super: {recommended_batch}")| Task | Performance | Comparison |
|---|---|---|
| ResNet-50 Training (imgs/sec) | 950 | 20% faster than 4070 |
| BERT-Large Inference (sentences/sec) | 1,650 | 20% faster than 4070 |
| Stable Diffusion (512x512, sec/img) | 4.8 | 15% faster than 4070 |
| LLaMA-7B Inference (tokens/sec) | 48 | 20% faster than 4070 |
| cuBLAS SGEMM 8192x8192 (TFLOPS) | 34 | 96% efficiency |
| Memory Bandwidth (GB/s measured) | 480 | 95% efficiency |
| Use Case | Rating | Notes |
|---|---|---|
| Deep Learning Training | Fair | 12GB limits model size |
| ML Inference | Excellent | Great FP8 performance at $599 |
| Scientific Computing | Good | Good FP32 for price |
| Video Processing | Excellent | Full NVENC with AV1 |
| Development/Prototyping | Excellent | Best entry Ada for CUDA dev |
| LLM Inference | Fair | 12GB limits to 7B quantized |
For inference and training smaller models (up to 3B parameters), 12GB is workable. For larger models, consider 4070 Ti Super with 16GB or used RTX 3090 with 24GB.
If you need a GPU now, 4070 Super offers excellent value. RTX 50 series may have better efficiency but will likely cost more. 4070 Super is a solid choice for current workloads.
4070 Super is about 10-15% faster than RTX 3080 with better efficiency. Both have similar VRAM (12GB vs 10/12GB). 4070 Super has FP8 and newer features.
Yes, 12GB is sufficient for training LoRAs and fine-tuning smaller models. Full SDXL fine-tuning is tight but possible with optimization. Consider 16GB for more headroom.
16GB, $200 more
20% slower, same price tier
Similar perf, 10/12GB, used
24GB, similar perf, used market
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