Choosing the right GPU is a crucial step when entering any AI-related field, and image generation, in particular, demands substantial computing power. To achieve optimal results, you’ll want the best GPU your budget allows. In this article, we will mainly focus on image generation applications, like Automatic 1111 (Stable Diffusion’s web version), but these GPUs are universally great choices for AI.
When considering which card to purchase, several key factors should be taken into account, and these can truly make a difference:
– Number of processing units: This metric signifies the raw GPU horsepower, directly influencing its task performance. Generally, the more processing units it has, the more iterations per second that it will be able to make.
VRAM: Stands for Video Random Access Memory. It is used to store graphical data, including textures, frame buffers, and other visual elements that are crucial for rendering images, videos and 3D elements. With more VRAM, you can craft higher-resolution images and utilize larger batch sizes to expedite your generation processes.
– Compatibility: At present, NVIDIA maintains its position as the dominant player in the field. Most AI image generation applications are exclusively compatible with NVIDIA graphics cards. Ideally, you’d want a GPU that offers broader software compatibility.
While there are industry-grade GPUs that are much more effective at machine learning and AI processing, these are also priced way outside the budget of most consumers (Like the H100 costing upwards of $35,000!) So, we will be concentrating on consumer-grade GPUs accessible to more people. Whether you’re a newcomer to the AI world or poised to make a substantial investment, these GPUs excel in empowering your creative and research endeavors.
1. NVIDIA RTX 4090
- Cores: 16,384
- VRAM: 24GB
- Memory type: GDDR6X
- Bus width: 384 bit
- Tensor Cores: 512
- Iterations per second (Automatic 1111): 21.04
The Nvidia GeForce RTX 4090, employing the Ada Lovelace architecture and the AD102 chip, stands as the top-tier desktop GPU for gaming, and it is no different when it comes to AI. With 16,384 out of 18,432 cores, it boasts 24 GB of GDDR6X graphics memory, linked via a 384-bit memory bus and clocked at 21 Gbits.
The card excels in raytracing, aided by 128 dedicated ray tracing cores.
But what interests us the most are the RTX 4090’s 512 Tensor Cores.
Tensor cores, a specialized feature of Nvidia GPUs, are designed for dynamic calculations and mixed-precision computing. These cores provide a significant boost in performance while maintaining precision. The term “Tensor” defines a data type that can hold or represent all forms of data. In our case, image generation data
This the fastest consumer grade GPU money can buy, and will yield amazing results.The RTX 4090 excels for AI art generation due to its unmatched capabilities, offering the perfect balance of power and precision, making it a prime choice for creative tasks.
Its expansive memory and specialized AI features provide artists with the tools to bring their creative visions to life with exceptional efficiency
RTX 4090
2. NVIDIA RTX 4080
- Cores: 9,728
- VRAM: 16 GB
- Memory type: GDDR6X
- Bus width: 256 bit
- Tensor Cores: 304
- Iterations per second (Automatic 1111): 19.41
The NVIDIA GeForce RTX 4080, released in September 2022, is an enthusiast-grade graphics card based on the 5 nm process with DirectX 12 Ultimate support. It features 9728 shading units, 16 GB GDDR6X memory, and offers a base clock of 2205 MHz (boosting up to 2505 MHz)
With fewer tensor cores and less memory than its top-of-the-line companion, the 4080 is still the next best alternative for AI generation, with any of the most common stable diffusion integrations. You would be slightly more limited in terms of resolution and batch size compared to the 4090, with 16GB of VRAM, but its raw power and ample tensor core count will still be more than sufficient for most consumers.
RTX 4080
$1099.50NVME M.2 SSD
NVIDIA RTX 4070Ti
- Cores: 7,680
- VRAM: 12 GB
- Memory type: GDDR6X
- Bus width: 192 bit
- Tensor Cores: 240
- Iterations per second (Automatic 1111): 17.65
One of the best value options, the 4070Ti is a solid option to consider. It is much more affordable than the high end 4090, or 4080, but it still delivers amazing performance on both games and productivity tasks.
For AI, its 240 tensor cores provide massive performance boosts over its predecessor the 3070, and thanks to the new 4000 series architecture, it even outperforms the 3090 and 3090Ti when it comes to it/s.
If you are on a budget and look to get the best bang for the buck, the 4070Ti would be an option to consider.
RTX 4070 Ti
$779$800
NVIDIA RTX 3090
- Cores: 10,496
- VRAM: 24 GB
- Memory type: GDDR6X
- Bus width: 384 bit
- Tensor Cores: 328
- Iterations per second (Automatic 1111): 16.66
The old king of the Nvidia family, the 3090, is the top-of-the-line card of the previous generation. Although, as we could see, it’s outperformed by the 4070 Ti, it still provides great performance thanks to its 328 Tensor Cores and a huge 24GB of VRAM, the same as the 4090.
Not having enough VRAM is a common issue among Stable Diffusion users. If there is not enough memory for generated data to store, the program will either crash, or the generation will simply stop. Considering what type of images you will be creating and at what resolution will help you choose if you need the extra processing speed of the 4070 Ti or the huge VRAM capacity that the 3090 offers.
RTX 3090
$999$1100
NVIDIA RTX 4060 Ti
- Cores: 4,352
- VRAM: 16 GB
- Memory type: GDDR6
- Bus width: 128 bit
- Tensor Cores: 136
- Iterations per second (Automatic 1111): 12.32
Although there are some other options to consider between the 3090 and the 4060 Ti, the differences in terms of performance are not as significant. We wanted to include the 4060 Ti for what it can bring to the table in terms of value. It might not be as fast as the 4070 or above, but it still offers a version with 16GB of GDDR6 VRAM, something unusual for a card in this range.
To note, the 4060 Ti comes in an 8GB VRAM version. If you look up this card, make sure you always get results for its 16GB version.
The 4060 Ti outperforms its predecessor by nearly 40% in almost any task you give it, while having a price tag that is almost half of what any other card on this list costs. Starting at around the $400 USD mark, the 4060 Ti is the best value GPU on the market today. You will get solid generation times at around 12 it/s, and even though it won’t be as fast as the top competitors, the 16GB of VRAM will more than make up for that speed.
If you are only getting started with AI and want to try your hand at Stable Diffusion without breaking the bank, then the 4060 Ti is the card you want to try first.
RTX 4060 Ti
$499$500
Conclusion
Selecting the right GPU for AI image generation is a critical decision, and it depends on your specific needs and budget. From the powerhouse performance of the 4090 to the remarkable value offered by the 4060 Ti, each card has its strengths and advantages.
The 4070 and 4080 bridge the gap between high-end performance and affordability, making them solid choices for a wide range of users. Remember to consider the factors we discussed, like VRAM capacity, processing speed, and cost when making your decision.
Whether you’re a seasoned AI enthusiast or just getting started, there’s a GPU on this list that can help you bring your creative visions to life. Ultimately, the best GPU for you is the one that aligns with your goals and your pocket, making your AI image generation journey both rewarding and cost-effective.
