Introduction

When selecting a GPU for AI, machine learning (ML), and deep learning (DL) development, choosing the right hardware can significantly impact your project's success. This article delves into the performance and pricing comparison between the NVIDIA GeForce TITAN X and the NVIDIA GeForce GTX 1080 Ti, highlighting the pros and cons of each to help you make an informed decision.

Performance Comparison

The NVIDIA TITAN X has slightly higher performance compared to the GTX 1080 Ti. However, the GTX 1080 Ti offers better performance per dollar, making it a more cost-effective choice.

Titan X outperforms the GTX 1080 Ti by about 11% in training networks such as ResNet-50, ResNet-152, Inception v3, and Inception v4. For instance, it completes training these networks approximately 11% faster. This increased performance can be attributed to the TITAN X's 12 GB VRAM, which is just 1 GB more than the GTX 1080 Ti's 11 GB VRAM.

Price and Value

The TITAN X comes with a price tag of around $1200, whereas the GTX 1080 Ti retails at $699. This means the TITAN X is about 72% more expensive and only 11% faster. In practical terms, the TITAN X is not worth the significant premium it commands.

Multi-GPU Training

For deep learning projects, using multiple GPUs can drastically increase performance. For instance, using two GTX 1080 Ti GPUs for multi-GPU training will be significantly faster than a single TITAN X. The advantage of combining multiple GPUs is their ability to parallelize tasks, which can greatly reduce training time.

Alternative: Radeon RX 2080 Ti

While the RX 2080 Ti might offer higher performance, the advantages may not justify its higher cost. The RX 2080 Ti is approximately 10-15% faster in some benchmarks, but it comes at a much higher price point, making it less cost-effective for most users.

NVIDIA GPU Architecture

NVIDIA uses two distinct architectures: GP100 and GP102. GP102 powers the NVIDIA GeForce cards, while GP100 powers the Tesla cards. Both GP100 and GP102 offer the same FP32 performance. GP102 is used by the GTX 1080 Ti, and the rest of the GeForce lineup uses a cut-down version of GP102.

Training vs. Inference

The decision between the TITAN X and GTX 1080 Ti also depends on the task being performed. If you are conducting FP32 training or inference, there is little difference between the two GPUs. However, if you are doing FP16 training, the performance can be significantly reduced due to reduced data precision. Both the TITAN X and GTX 1080 Ti perform well in FP32 inference, making them suitable for this application.

Conclusion

Given the price-performance ratio and the ability to significantly increase performance through multi-GPU setups, the NVIDIA GeForce GTX 1080 Ti is the more cost-effective and practical choice for AI, ML, and DL development.

Benchmark Graphs

Check out the benchmark graphs from Lambda Labs for detailed performance comparisons.