Which GPU is Better for Deep Learning: GTX 1050 Ti 4GB or GTX 1060 3GB?

Introduction

When it comes to choosing between the NVIDIA GTX 1050 Ti 4GB and the NVIDIA GTX 1060 3GB for deep learning applications, there are several factors to consider, from performance to budget. This guide will help you understand the differences and make an informed decision.

Performance in Deep Learning Applications

The NVIDIA GTX 1060 is significantly more powerful than the GTX 1050 Ti, primarily due to its higher number of CUDA cores (1280 vs. 768) and higher clock speeds. These features make the GTX 1060 a far superior choice for deep learning applications.

Even though the GTX 1060 has only 3GB of GDDR5 VRAM, it still outperforms the 4GB VRAM in the GTX 1050 Ti. The GTX 1050 Ti, with its 1GB less VRAM, is generally not recommended for deep learning tasks, especially when dealing with large datasets.

Game Performance and Cryptocurrency Mining

The GTX 1060 is also a better option for gamers, as it can run most modern AAA titles at 1080p settings with ease. Additionally, the GTX 1060 is one of the top choices for cryptocurrency mining, particularly with its high hash/watt numbers for mining Zcash and Ethereum, thanks to its improved VRAM and high clock speed.

Cost Considerations

The GTX 1060 6GB is the most optimal choice for deep learning and gamers alike, offering even more VRAM and higher performance. While it may cost a bit more, the extra 3GB of VRAM can be crucial for various deep learning projects, especially those involving extensive model training and data processing.

Conclusion and Recommendations

Considering the overall performance, cost, and utility, the NVIDIA GTX 1060 3GB is the better choice for deep learning applications. However, if you have the budget, the NVIDIA GTX 1060 6GB is the ideal option.

For those on a tight budget, the GTX 1050 Ti 4GB may suffice for basic deep learning tasks, but for the best performance and versatility, the GTX 1060 3GB is recommended.