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The NVIDIA GeForce RTX 4070 Ti Super delivers a significant upgrade over the original 4070 Ti with 16GB VRAM (up from 12GB) and more CUDA cores. Built on Ada Lovelace architecture, it addresses the biggest complaint about the original card - limited memory. For CUDA developers, the 16GB GDDR6X memory opens up training and inference workloads that were constrained on the 12GB 4070 Ti. Combined with 4th generation Tensor Cores and FP8 support, it offers excellent value for ML workloads. This guide covers the RTX 4070 Ti Super's specifications, CUDA optimization strategies, and practical tips for maximizing performance.
| Architecture | Ada Lovelace (AD103) |
| CUDA Cores | 8,448 |
| Tensor Cores | 264 |
| Memory | 16GB GDDR6X |
| Memory Bandwidth | 672 GB/s |
| Base / Boost Clock | 2340 / 2610 MHz |
| FP32 Performance | 44.1 TFLOPS |
| FP16 Performance | 88.2 TFLOPS |
| L2 Cache | 48MB |
| TDP | 285W |
| NVLink | No |
| MSRP | $799 |
| Release | January 2024 |
This code snippet shows how to detect your RTX 4070 Ti Super, check available memory, and configure optimal settings for the Ada Lovelace (AD103) architecture.
import torch
import pynvml
# Check if RTX 4070 Ti 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 Ti Super Memory: 16GB - Optimal batch sizes
# Architecture: Ada Lovelace (AD103)
# CUDA Cores: 8,448
# Memory-efficient training for RTX 4070 Ti Super
torch.backends.cuda.matmul.allow_tf32 = True # Enable TF32 for Ada Lovelace (AD103)
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 / 16 GB total")
# Recommended batch size calculation for RTX 4070 Ti Super
model_memory_gb = 2.0 # Adjust based on your model
batch_multiplier = (16 - model_memory_gb) / 4 # 4GB per batch unit
recommended_batch = int(batch_multiplier * 32)
print(f"Recommended batch size for RTX 4070 Ti Super: {recommended_batch}")| Task | Performance | Comparison |
|---|---|---|
| ResNet-50 Training (imgs/sec) | 1,100 | 15% faster than 4070 Ti |
| BERT-Large Inference (sentences/sec) | 1,950 | Similar to 4070 Ti |
| Stable Diffusion (512x512, sec/img) | 4.2 | Larger batch possible |
| LLaMA-7B Inference (tokens/sec) | 55 | Similar to 4070 Ti |
| cuBLAS SGEMM 8192x8192 (TFLOPS) | 42 | 95% efficiency |
| Memory Bandwidth (GB/s measured) | 640 | 95% efficiency |
| Use Case | Rating | Notes |
|---|---|---|
| Deep Learning Training | Good | 16GB enables larger models than 4070 Ti |
| ML Inference | Excellent | Great FP8 performance at $799 |
| Scientific Computing | Good | Solid FP32 for simulations |
| Video Processing | Excellent | Full NVENC with AV1 |
| Development/Prototyping | Excellent | Best value for 16GB Ada |
| LLM Inference | Good | 16GB handles quantized 13B models |
Yes, 16GB handles most ML training and inference workloads. You can train models up to ~6B parameters with mixed precision, and run inference on 7B-13B LLMs with quantization.
If budget allows, 4080 Super is 20% faster with faster memory. 4070 Ti Super offers better value at $200 less with the same 16GB VRAM - ideal for memory-constrained workloads.
4070 Ti Super has similar compute to RTX 3090 but 16GB vs 24GB VRAM. For memory-heavy workloads, used 3090 may be better. For efficiency and modern features, 4070 Ti Super wins.
Yes, the 16GB VRAM handles SDXL comfortably with room for larger batch sizes and LoRA training that was tight on the 12GB 4070 Ti.
20% faster, $200 more
Only 12GB, being phased out
12GB, $200 less
24GB, similar compute, used market
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