Best Budget GPU for Stable Diffusion

Sujeet Kumar
8 Min Read

Stable Diffusion is a new AI-powered image generation tool that can create realistic and detailed images from text descriptions. However, it requires a powerful GPU to run efficiently. In this article, we will discuss the best budget GPUs for Stable Diffusion, based on price, performance, and VRAM capacity.

We will also provide a brief overview of the Stable Diffusion algorithm and its requirements. By the end of this article, you will know which GPU is the best fit for your budget and needs.

Here are some of the things to consider when choosing a GPU for Stable Diffusion:

  • Price: Stable Diffusion is a demanding application, so you will need a powerful GPU. However, you don’t need to spend a fortune. There are some great budget GPUs that can handle Stable Diffusion without breaking the bank.
  • Performance: The GPU’s performance will affect the speed at which Stable Diffusion can generate images. A faster GPU will be able to generate images more quickly.
  • VRAM capacity: Stable Diffusion requires a minimum of 6GB of VRAM, But 8GB Should be the recommended. However, if you want to work with larger image sizes, you will need a GPU with more VRAM.

Nvidia GeForce RTX 2060

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Memory Size [VRAM]12 GB
Clock1365 MHz – 1680 MHz
Process Size12 nm
TDP160 W

The Nvidia GeForce RTX 2060 comes in two variations, offering 6 GB and 12 GB of VRAM. Opting for the 12 GB version is advisable for tasks like stable diffusion and demanding AI text-to-image generation processes. This is due to its memory clock, which ranges from 1365 MHz to 1680 MHz, and its 12 nm fabrication process. The GPU delivers commendable performance and operates efficiently with a 160 W TDP.

For stable diffusion, a minimum of 6 GB VRAM is necessary, enabling the 6 GB variant to handle images up to 768×768. However, for optimal performance with larger image sizes, such as 1024×1024 and beyond, the 12 GB variant is more suitable. It’s worth considering that while this GPU performs well, the evolving landscape of GPUs, such as the 30 and 40 series, should be weighed against budget constraints and performance expectations.

AMD Radeon RX 6600 XT

image 25 - Best Budget GPU for Stable Diffusion
Memory Size8 GB
Clock1968 MHz – 2359 MHz
Process Size7 nm
TDP160 W

The AMD Radeon RX 6600 XT is designed for gaming and resource-intensive tasks, boasting 8 GB of VRAM and clock speeds ranging from 1968 MHz to 2359 MHz. Built on a 7 nm process, it strikes a balance between performance and power efficiency with a 160 W TDP. While the 8 GB VRAM is sufficient for stable diffusion tasks with moderate complexity, it’s important to note that higher VRAM variants (e.g., 12 GB) can handle more intricate models found in newer AI text-to-image generators.

Regarding affordability, the RX 6600 XT can be appealing if obtained at a reasonable cost. However, it’s worth considering that newer GPU models like Nvidia’s 30 or 40 series might offer better performance. The decision hinges on budget constraints and hardware investment willingness. Additionally, keep in mind that AMD GPUs may not offer the same level of compatibility as Nvidia cards do at present.

AMD Radeon RX 6650 XT

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Memory Size8 GB
Clock2055 MHz – 2410 MHz
Process Size7 nm
TDP176 W

The AMD Radeon RX 6650 XT is tailored for demanding tasks, notably gaming and advanced applications like the stable diffusion AI. It’s 8 GB VRAM and clock speeds ranging from 2055 MHz to 2410 MHz equip it for stable diffusion tasks that require substantial VRAM capacity. However, for more advanced stable diffusion models, a 12 GB variant could be beneficial. While the card offers good value if acquired inexpensively, it’s worth considering newer-gen GPUs like the 30 or 40 series for enhanced performance. It’s important to note that AMD GPUs might require some optimization effort compared to Nvidia cards, although this could potentially improve with AMD’s increased focus on AI. In the context of stable diffusion’s VRAM demands (at least 6 GB for basic models and 12 GB for more advanced ones), the Radeon RX 6650 XT is a viable option, provided that its price aligns with your budget and preferences.

Nvidia GeForce RTX 3060

image 6 - Best Budget GPU for Stable Diffusion
Memory Size12 GB
Clock1320 MHz – 1777 MHz
Process Size8 nm
TDP170 W

The Nvidia GeForce RTX 3060 emerges as a prominent choice for stable diffusion tasks in AI, thanks to its substantial 12 GB memory size. With a clock speed ranging from 1320 MHz to 1777 MHz and manufactured on an efficient 8 nm process, this GPU offers a commendable balance between performance and power consumption, with a TDP of 170 W. Notably, its ample VRAM is well-suited for accommodating the demands of stable diffusion, capable of handling tasks like 1024×1024 image generation with ease. the RTX 3060 remains a compelling option, especially if obtained at a favorable price point. It’s worth considering budget constraints and AI project requirements when evaluating the optimal GPU for stable diffusion tasks. It’s worth noting that while AMD GPUs are viable alternatives, Nvidia’s current edge in AI operations might influence your decision.

Nvidia GeForce RTX 3060 Ti

image 20 - Best Budget GPU for Stable Diffusion
Memory Size8 GB
Clock1410 MHz – 1665 MHz
Process Size8 nm 200 W
TDP200 W

The Nvidia GeForce RTX 3060 Ti is a capable option for stable diffusion tasks in AI, featuring 8 GB of memory. With a clock speed ranging from 1410 MHz to 1665 MHz and built on an efficient 8 nm process, this GPU provides a balance between performance and efficiency, operating within a 200 W TDP. While a 6 to 8 GB VRAM configuration can handle 512×512 images, the 8 GB VRAM in this model accommodates more advanced tasks like 1024×1024 image generation. Despite its prowess, it may not match the RTX 3060 in VRAM capacity, which AI tasks often rely on. Suitability should be considered based on budget constraints and project needs, weighing it against newer generation GPUs like the 30 and 40 series. Keep in mind Nvidia’s current advantage in AI, although AMD GPUs are an option, with the potential for improved AI compatibility.

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