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The NVIDIA GeForce RTX 4080 delivers exceptional CUDA performance in a more accessible package than the flagship RTX 4090. Built on Ada Lovelace architecture with 9,728 CUDA cores and 16GB GDDR6X memory, it offers an excellent balance of compute power and efficiency. For CUDA developers, the RTX 4080 provides approximately 70% of RTX 4090 performance while consuming 130W less power. The 16GB VRAM handles most machine learning models, and the 4th generation Tensor Cores deliver strong inference performance with FP8 precision support. This guide covers the RTX 4080's specifications, CUDA optimization strategies, benchmark results, and practical tips for maximizing performance in your GPU kernels.
| Architecture | Ada Lovelace (AD103) |
| CUDA Cores | 9,728 |
| Tensor Cores | 304 |
| Memory | 16GB GDDR6X |
| Memory Bandwidth | 717 GB/s |
| Base / Boost Clock | 2205 / 2505 MHz |
| FP32 Performance | 48.7 TFLOPS |
| FP16 Performance | 97.5 TFLOPS |
| L2 Cache | 64MB |
| TDP | 320W |
| NVLink | No |
| MSRP | $1,199 |
| Release | November 2022 |
This code snippet shows how to detect your RTX 4080, check available memory, and configure optimal settings for the Ada Lovelace (AD103) architecture.
import torch
import pynvml
# Check if RTX 4080 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 Memory: 16GB - Optimal batch sizes
# Architecture: Ada Lovelace (AD103)
# CUDA Cores: 9,728
# Memory-efficient training for RTX 4080
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
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: {recommended_batch}")| Task | Performance | Comparison |
|---|---|---|
| ResNet-50 Training (imgs/sec) | 1,320 | 30% faster than RTX 3080 |
| BERT-Large Inference (sentences/sec) | 2,250 | 70% of RTX 4090 |
| Stable Diffusion (512x512, sec/img) | 3.9 | 35% faster than RTX 3080 |
| LLaMA-7B Inference (tokens/sec) | 62 | 73% of RTX 4090 |
| cuBLAS SGEMM 8192x8192 (TFLOPS) | 46.2 | 95% of theoretical peak |
| Memory Bandwidth (GB/s measured) | 675 | 94% of theoretical peak |
| Use Case | Rating | Notes |
|---|---|---|
| Deep Learning Training | Good | 16GB limits large models but excellent for most research workloads |
| ML Inference | Excellent | Great performance per watt for deployment scenarios |
| Scientific Computing | Good | Strong FP32 performance, 16GB may limit some simulations |
| Video Processing | Excellent | Full NVENC capabilities, more accessible price point |
| Multi-GPU Training | Fair | No NVLink, but dual 4080s cost less than one 4090 |
| Development/Prototyping | Excellent | Perfect for developing kernels before datacenter deployment |
For most ML tasks, 16GB is sufficient. You can train models up to ~3B parameters with full precision or ~6B with mixed precision and gradient checkpointing. For larger models, consider quantization techniques or the RTX 4090 with 24GB.
If you work with large models (>7B parameters) or need maximum throughput, get the RTX 4090. For most development, research, and inference workloads, the RTX 4080 offers better value at $400 less.
The RTX 4080 is approximately 30-40% faster than RTX 3090 in most ML tasks, uses less power (320W vs 350W), and has 4th gen Tensor Cores with FP8. However, RTX 3090 has 24GB VRAM vs 16GB, which matters for large models.
Yes, the RTX 4080 runs Stable Diffusion extremely well. With 16GB VRAM, it handles SDXL at full resolution and generates 512x512 images in under 4 seconds. FP16 mode maximizes performance.
Excellent for LLM inference. The 16GB VRAM handles quantized 7B-13B models efficiently. FP8 Tensor Cores and the large L2 cache make it ideal for production inference workloads with strong performance per watt.
43% faster with 24GB VRAM at $400 more
25% slower but $400 less, 12GB VRAM
Slower but 24GB VRAM, good used prices
Datacenter option with 40/80GB HBM2e
Ready to optimize your CUDA kernels for RTX 4080? Download RightNow AI for real-time performance analysis.