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The NVIDIA GeForce RTX 2080 Ti was the flagship of the Turing generation, featuring 4,352 CUDA cores and 11GB GDDR6 memory. As an older card, it still provides reasonable CUDA performance with 1st generation Tensor Cores, though lacking modern features like TF32 and FP8. For CUDA developers, the RTX 2080 Ti remains relevant in the used market due to its 11GB VRAM capacity and solid FP32 compute. While slower than newer cards and lacking advanced Tensor Core features, it handles inference and classical CUDA workloads at attractive used prices. This guide covers realistic expectations for the RTX 2080 Ti in 2025, optimization strategies for Turing architecture, and use cases where it still makes sense.
| Architecture | Turing (TU102) |
| CUDA Cores | 4,352 |
| Tensor Cores | 544 |
| Memory | 11GB GDDR6 |
| Memory Bandwidth | 616 GB/s |
| Base / Boost Clock | 1350 / 1545 MHz |
| FP32 Performance | 13.4 TFLOPS |
| FP16 Performance | 26.9 TFLOPS |
| L2 Cache | 5.5MB |
| TDP | 250W |
| NVLink | Yes |
| MSRP | $999 |
| Release | September 2018 |
This code snippet shows how to detect your RTX 2080 Ti, check available memory, and configure optimal settings for the Turing (TU102) architecture.
import torch
import pynvml
# Check if RTX 2080 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 2080 Ti Memory: 11GB - Optimal batch sizes
# Architecture: Turing (TU102)
# CUDA Cores: 4,352
# Memory-efficient training for RTX 2080 Ti
torch.backends.cuda.matmul.allow_tf32 = True # Enable TF32 for Turing (TU102)
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 / 11 GB total")
# Recommended batch size calculation for RTX 2080 Ti
model_memory_gb = 2.0 # Adjust based on your model
batch_multiplier = (11 - model_memory_gb) / 4 # 4GB per batch unit
recommended_batch = int(batch_multiplier * 32)
print(f"Recommended batch size for RTX 2080 Ti: {recommended_batch}")| Task | Performance | Comparison |
|---|---|---|
| ResNet-50 Training FP16 (imgs/sec) | 520 | Requires explicit FP16 |
| BERT-Base Inference FP16 (sentences/sec) | 850 | Adequate for inference |
| Stable Diffusion (512x512, sec/img) | 9.5 | Works but slow vs newer cards |
| LLaMA-7B Inference (tokens/sec) | 18 | Requires int8 quantization |
| cuBLAS SGEMM 4096x4096 (TFLOPS) | 12.7 | 95% of theoretical peak |
| Memory Bandwidth (GB/s measured) | 578 | 94% of theoretical peak |
| Use Case | Rating | Notes |
|---|---|---|
| Learning CUDA | Good | 11GB helps, but older architecture |
| Classical CUDA | Good | Good FP32 for traditional compute |
| ML Inference | Fair | FP16 Tensor Cores work, but slower |
| Small Model Training | Fair | 11GB helps, but slow vs Ampere+ |
| Modern DL Training | Poor | Lacks TF32, BF16, very slow |
| Budget Experimentation | Good | Good used prices with 11GB |
For inference and learning, yes. For training, it is significantly slower than Ampere+ cards due to lack of TF32 and slower Tensor Cores. Good used value if priced under $300, but limited for modern DL.
RTX 3060 Ti is faster for ML due to TF32 and better Tensor Cores, despite having 8GB vs 11GB. Choose 2080 Ti only if significantly cheaper and you need the extra 3GB VRAM.
Yes, CUDA 12 and modern frameworks support Turing (CC 7.5). However, lack of TF32 means mixed-precision training is slower. Works fine for inference and classical CUDA workloads.
Under $300 is good value for the 11GB VRAM. Above $350, consider RTX 3060 Ti or 3070 instead. The 2080 Ti makes sense only at significant discount vs newer cards.
Faster, TF32, but only 8GB
Much faster, modern features
Similar but less VRAM, cheaper
Newer, 12GB, better for ML
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