The Most Affordable GPUs for Deep Learning in 2023
Deep learning has gained immense popularity and accessibility in recent years. However, training deep learning models can require substantial computational power, which can be challenging for those on a budget. In this article, we will focus on the top 3 cheapest GPUs for deep learning available in 2023, considering not only our previous knowledge but also the overall recommendations for the best GPUs in various categories.
If you want to run models like Stable Diffusion and Openjourney, these GPU cards are for you.
The Top 3 Cheapest GPUs for Deep Learning
- NVIDIA GeForce RTX 2060 — Cheapest GPU for Deep Learning Beginners
- NVIDIA GeForce RTX 3060 (12GB) — Best Affordable Entry Level GPU for Deep Learning
- NVIDIA GeForce GTX 1660 Super — Budget-Friendly Alternative for Basic Deep Learning Tasks
1. NVIDIA GeForce RTX 2060 — Cheapest GPU for Deep Learning Beginners
The NVIDIA GeForce RTX 2060 is an excellent entry-level GPU for deep learning, especially if you’re a beginner and on a budget.
Key Specs:
- Memory: 6GB DDR6
- CUDA Cores: 1920
- Tensor Cores: 240
- Architecture: Turing
- Memory Bandwidth: 336GBps
- Slot Width: Dual-slot
- TDP: 160W
The RTX 2060 is the cheapest GPU for deep learning beginners, with 6GB of VRAM, which is sufficient for training most simple models. You can always upgrade to a more powerful GPU later on if needed.
Pros:
- Budget-friendly and good value
- Great for beginners
Cons:
- 6GB of RAM might not be enough for more complex models
- Not as powerful as the other GPUs on this list
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2. NVIDIA GeForce RTX 3060 (12GB) — Best Affordable Entry Level GPU for Deep Learning
The NVIDIA GeForce RTX 3060 is the best affordable GPU for deep learning right now. It has 12GB of VRAM, which is one of the sweet spots for training deep learning models.
Key Specs:
- Memory: 12GB DDR6
- CUDA Cores: 3584
- Tensor Cores: 112
- Architecture: Ampere
- Memory Bandwidth: 360GB/s
- Slot Width: Dual-slot
- TDP: 170W
Pros:
- Affordable and good value
- Great for beginners
- 12GB of RAM is a good sweet spot for training models
Cons:
- Not as powerful as other 30 series GPUs
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3. NVIDIA GeForce GTX 1660 Super — Budget-Friendly Alternative for Basic Deep Learning Tasks
The NVIDIA GeForce GTX 1660 Super is a budget-friendly alternative for basic deep learning tasks. While it lacks Tensor cores found in the RTX series, it’s still suitable for simple deep learning tasks.
Key Specs:
- Memory: 6GB GDDR6
- CUDA Cores: 1408
- Architecture: Turing
- Memory Bandwidth: 336GBps
- Slot Width: Dual-slot
- TDP: 125W
Pros:
- Budget-friendly
- Suitable for basic deep learning tasks
Cons:
- Lacks Tensor cores found in RTX series
- 6GB of RAM might not be enough for more complex models
When choosing the most affordable GPU for deep learning, it’s essential to consider the pros and cons of each option. Your choice will depend on your specific needs and requirements, such as the complexity of the models you will be working on, your budget constraints, and your willingness to make trade-offs in terms of performance and capabilities.
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Comparison of these top 3
To help you better understand the differences between the top 3 cheapest GPUs for deep learning in 2023, I have compiled a comparison table and an analysis of the key features and performance aspects.
| GPU Model | Memory | CUDA Cores | Tensor Cores | Architecture | Memory Bandwidth | Slot Width | TDP |
|-----------------------------|--------|------------|--------------|--------------|------------------|------------|------|
| NVIDIA GeForce RTX 2060 | 6GB | 1920 | 240 | Turing | 336GBps | Dual-slot | 160W |
| NVIDIA GeForce RTX 3060 | 12GB | 3584 | 112 | Ampere | 360GB/s | Dual-slot | 170W |
| NVIDIA GeForce GTX 1660 Super | 6GB | 1408 | N/A | Turing | 336GBps | Dual-slot | 125W |Memory
The NVIDIA GeForce RTX 3060 comes with 12GB of VRAM, double the memory compared to the NVIDIA GeForce RTX 2060 and GTX 1660 Super, which both have 6GB of VRAM. The increased memory in the RTX 3060 allows for more complex models to be trained.
CUDA Cores and Tensor Cores
The RTX 3060 has the highest number of CUDA cores, which contributes to better parallel processing performance in deep learning tasks. The RTX 2060 and RTX 3060 both have Tensor cores, which provide significant speedups for deep learning applications. The GTX 1660 Super lacks Tensor cores, making it less efficient for deep learning tasks compared to the RTX series.
Architecture
The RTX 3060 uses the newer Ampere architecture, while the RTX 2060 and GTX 1660 Super use the older Turing architecture. The Ampere architecture offers improved performance and power efficiency compared to the Turing architecture.
Memory Bandwidth
The RTX 3060 has slightly better memory bandwidth than the RTX 2060 and GTX 1660 Super. The higher memory bandwidth ensures faster data transfer and contributes to improved performance in deep learning tasks.
Thermal Design Power (TDP)
The TDP of a GPU indicates the amount of heat it generates and the power it consumes. The GTX 1660 Super has the lowest TDP at 125W, making it the most energy-efficient option among the three. The RTX 2060 has a TDP of 160W, while the RTX 3060 has a slightly higher TDP of 170W.
Conclusion
In conclusion, the NVIDIA GeForce RTX 3060 offers the best overall value among the top 3 cheapest GPUs for deep learning, with its higher memory capacity, CUDA cores, and newer architecture. The NVIDIA GeForce RTX 2060 serves as a good entry-level GPU for beginners with its Tensor cores and decent performance. Lastly, the NVIDIA GeForce GTX 1660 Super is a budget-friendly alternative suitable for basic deep learning tasks, although it lacks Tensor cores and has a lower number of CUDA cores.
