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The NVIDIA GeForce RTX 3080 Ti represents the high-end of the Ampere consumer lineup, delivering 10,240 CUDA cores and 12GB GDDR6X memory. Positioned between the RTX 3080 and RTX 3090, it offers near-flagship performance at a more accessible price point. For CUDA developers, the RTX 3080 Ti provides excellent compute performance with 3rd generation Tensor Cores supporting TF32, FP16, and INT8 operations. The 12GB VRAM capacity is adequate for most ML workloads, though the lack of FP8 support compared to newer Ada cards is a consideration. This guide covers the RTX 3080 Ti's specifications, CUDA optimization strategies, benchmark results, and practical tips for maximizing performance in Ampere architecture workflows.
| Architecture | Ampere (GA102) |
| CUDA Cores | 10,240 |
| Tensor Cores | 320 |
| Memory | 12GB GDDR6X |
| Memory Bandwidth | 912 GB/s |
| Base / Boost Clock | 1365 / 1665 MHz |
| FP32 Performance | 34.1 TFLOPS |
| FP16 Performance | 68.2 TFLOPS |
| L2 Cache | 6MB |
| TDP | 350W |
| NVLink | No |
| MSRP | $1,199 |
| Release | June 2021 |
This code snippet shows how to detect your RTX 3080 Ti, check available memory, and configure optimal settings for the Ampere (GA102) architecture.
import torch
import pynvml
# Check if RTX 3080 Ti 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 Ti Memory: 12GB - Optimal batch sizes
# Architecture: Ampere (GA102)
# CUDA Cores: 10,240
# Memory-efficient training for RTX 3080 Ti
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 / 12 GB total")
# Recommended batch size calculation for RTX 3080 Ti
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 3080 Ti: {recommended_batch}")| Task | Performance | Comparison |
|---|---|---|
| ResNet-50 Training (imgs/sec) | 1,280 | 95% of RTX 3090 |
| BERT-Large Inference (sentences/sec) | 1,850 | Strong Ampere performance |
| Stable Diffusion (512x512, sec/img) | 4.8 | Good for generation tasks |
| LLaMA-7B Inference (tokens/sec) | 48 | Solid with quantization |
| cuBLAS SGEMM 8192x8192 (TFLOPS) | 32.1 | 94% of theoretical peak |
| Memory Bandwidth (GB/s measured) | 856 | 94% of theoretical peak |
| Use Case | Rating | Notes |
|---|---|---|
| Deep Learning Training | Good | 12GB handles most models, TF32 acceleration |
| ML Inference | Good | No FP8, but strong FP16 performance |
| Scientific Computing | Excellent | Strong FP32/FP64 compute |
| Video Processing | Excellent | NVENC, good memory for complex projects |
| Large Language Models | Fair | 12GB limits to ~7B parameters |
| Multi-GPU Training | Fair | No NVLink, PCIe 4.0 only |
For ML work, the RTX 3090's 24GB VRAM is often worth the premium over 12GB. Performance is nearly identical. If your models fit in 12GB, the 3080 Ti offers better value. Check used prices carefully.
RTX 4070 Ti is slightly faster, has FP8 support, and 72MB L2 cache but same 12GB VRAM. If prices are similar, get the 4070 Ti for Ada features. 3080 Ti makes sense only at significant discount.
Still capable for ML but showing age. The 12GB VRAM and lack of FP8 limit it vs newer cards. Good used value if price is right, but consider RTX 4070 Ti or 4080 for new purchases.
Compute Capability 8.6 (Ampere). Supports all CUDA 11/12 features except FP8 Tensor operations which require Ada (CC 8.9) or newer. TF32 and BF16 are supported.
Nearly identical performance, 24GB VRAM
Newer architecture, FP8, large L2 cache
Significantly faster, modern features
More affordable, 8GB VRAM
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