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The NVIDIA GeForce RTX 4080 Super delivers enhanced performance over the original RTX 4080 with more CUDA cores and faster memory. Built on Ada Lovelace architecture with 10,240 CUDA cores and 16GB GDDR6X, it offers improved value for CUDA developers. For CUDA developers, the RTX 4080 Super provides approximately 10-15% more performance than the RTX 4080 at the same $999 MSRP. The 4th generation Tensor Cores with FP8 support deliver excellent inference performance, making it a strong choice for ML workloads. This guide covers the RTX 4080 Super's specifications, CUDA optimization strategies, benchmark results, and practical tips for maximizing performance.
| Architecture | Ada Lovelace (AD103) |
| CUDA Cores | 10,240 |
| Tensor Cores | 320 |
| Memory | 16GB GDDR6X |
| Memory Bandwidth | 736 GB/s |
| Base / Boost Clock | 2290 / 2550 MHz |
| FP32 Performance | 52.2 TFLOPS |
| FP16 Performance | 104.4 TFLOPS |
| L2 Cache | 64MB |
| TDP | 320W |
| NVLink | No |
| MSRP | $999 |
| Release | January 2024 |
This code snippet shows how to detect your RTX 4080 Super, check available memory, and configure optimal settings for the Ada Lovelace (AD103) architecture.
import torch
import pynvml
# Check if RTX 4080 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 4080 Super Memory: 16GB - Optimal batch sizes
# Architecture: Ada Lovelace (AD103)
# CUDA Cores: 10,240
# Memory-efficient training for RTX 4080 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 4080 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 4080 Super: {recommended_batch}")| Task | Performance | Comparison |
|---|---|---|
| ResNet-50 Training (imgs/sec) | 1,450 | 10% faster than RTX 4080 |
| BERT-Large Inference (sentences/sec) | 2,400 | 8% faster than RTX 4080 |
| Stable Diffusion (512x512, sec/img) | 3.5 | 10% faster than RTX 4080 |
| LLaMA-7B Inference (tokens/sec) | 68 | 10% faster than RTX 4080 |
| cuBLAS SGEMM 8192x8192 (TFLOPS) | 50 | 95% of theoretical peak |
| Memory Bandwidth (GB/s measured) | 700 | 95% of theoretical peak |
| Use Case | Rating | Notes |
|---|---|---|
| Deep Learning Training | Good | 16GB limits large models but great for most research |
| ML Inference | Excellent | FP8 Tensor Cores deliver strong inference |
| Scientific Computing | Good | Strong FP32 performance for simulations |
| Video Processing | Excellent | Full NVENC capabilities with AV1 |
| Development/Prototyping | Excellent | Great price/performance for dev work |
| LLM Inference | Good | 16GB handles 7B-13B quantized models |
No, the 10-15% improvement does not justify upgrading from RTX 4080. The Super is primarily for new buyers who get better value at the same price point.
Yes, it is excellent for ML inference and training of medium-sized models. The 16GB VRAM handles most workloads, and FP8 Tensor Cores provide strong inference performance.
RTX 4090 is approximately 35-40% faster with 24GB vs 16GB VRAM. For large model training or maximum throughput, the 4090 is better. For most work, 4080 Super offers better value.
NVIDIA recommends a 750W PSU. The 320W TDP is manageable, but a quality PSU with proper PCIe power delivery is important for stable CUDA workloads.
40% faster with 24GB, $600 more
Original, slightly slower
20% slower, $200 less
24GB, similar perf, good used prices
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