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The NVIDIA GeForce RTX 3080 offers excellent CUDA performance for its price point, making it a popular choice for ML practitioners and hobbyists. With 8,704 CUDA cores and 10GB (or 12GB in the later revision) of GDDR6X memory, it provides substantial compute power for training and inference. For CUDA developers, the RTX 3080 hits a sweet spot between performance and cost. While the 10GB VRAM limits large model training, it handles most inference workloads, smaller training jobs, and development tasks efficiently. This guide covers the RTX 3080's specifications, CUDA optimization strategies, benchmark results, and tips for working within its memory constraints.
| Architecture | Ampere (GA102) |
| CUDA Cores | 8,704 |
| Tensor Cores | 272 |
| Memory | 10GB GDDR6X |
| Memory Bandwidth | 760 GB/s |
| Base / Boost Clock | 1440 / 1710 MHz |
| FP32 Performance | 29.8 TFLOPS |
| FP16 Performance | 59.6 TFLOPS |
| L2 Cache | 5MB |
| TDP | 320W |
| NVLink | No |
| MSRP | $699 |
| Release | September 2020 |
This code snippet shows how to detect your RTX 3080, check available memory, and configure optimal settings for the Ampere (GA102) architecture.
import torch
import pynvml
# Check if RTX 3080 is available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Using device: {torch.cuda.get_device_name(0)}")
# RTX 3080 Memory: 10GB - Optimal batch sizes
# Architecture: Ampere (GA102)
# CUDA Cores: 8,704
# Memory-efficient training for RTX 3080
torch.backends.cuda.matmul.allow_tf32 = True # Enable TF32 for Ampere (GA102)
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 / 10 GB total")
# Recommended batch size calculation for RTX 3080
model_memory_gb = 2.0 # Adjust based on your model
batch_multiplier = (10 - model_memory_gb) / 4 # 4GB per batch unit
recommended_batch = int(batch_multiplier * 32)
print(f"Recommended batch size for RTX 3080: {recommended_batch}")| Task | Performance | Comparison |
|---|---|---|
| ResNet-50 Training (imgs/sec) | 980 | 77% of RTX 3090 |
| BERT-Large Inference (sentences/sec) | 1,180 | Strong for batch inference |
| Stable Diffusion (512x512, sec/img) | 5.8 | Handles SD 1.5 well |
| LLaMA-7B Inference (tokens/sec) | 28 | Requires 8-bit quantization |
| cuBLAS SGEMM 8192x8192 (TFLOPS) | 27.2 | 91% of theoretical peak |
| Memory Bandwidth (GB/s measured) | 710 | 93% of theoretical peak |
| Use Case | Rating | Notes |
|---|---|---|
| Deep Learning Training | Good | 10GB limits large models but excellent for smaller architectures |
| ML Inference | Excellent | Great performance per dollar for deployment |
| Development/Prototyping | Excellent | Fast iteration for model development |
| Stable Diffusion | Good | Handles SD 1.5, SDXL needs optimization |
| Gaming + ML Workstation | Excellent | Dual-purpose workstation GPU |
| LLM Inference | Fair | Requires quantization for 7B+ models |
For most development and inference, yes. Training is limited to models under ~3B parameters with mixed precision. For larger models, consider the 12GB variant or RTX 3090.
The 12GB variant is worth the premium for ML work. The extra 2GB helps with larger batch sizes and models at the edge of 10GB capacity. The 12GB also has slightly more CUDA cores.
Yes, RTX 3080 runs SD 1.5 smoothly. For SDXL, you may need to use FP16 and optimized samplers. 512x512 images generate in about 5-6 seconds.
With quantization, yes. 8-bit quantized 7B models run well. For 13B models, you will need 4-bit quantization. Larger models require multiple GPUs or offloading.
24GB VRAM and NVLink, 25% faster
Newer arch, 12GB, similar performance
35% slower but good value
45% faster, 16GB, next gen
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