Stable Diffusion XL: GPU, CPU, and RAM Requirements

To run Stable Diffusion XL (SDXL) smoothly, you’ll need a strong Nvidia graphics card with a minimum of 12GB VRAM and at least 32GB of RAM. These hardware requirements ensure optimal performance and fast image generation with SDXL.

Stable Diffusion XL (SDXL) is an enhanced version of the popular AI image generation model Stable Diffusion. It can create higher resolution and more detailed images.

However, running SDXL requires more powerful hardware than the original Stable Diffusion model. This article outlines the GPU, CPU, and RAM requirements to run SDXL smoothly.

Here, we’ll provide you with a comprehensive guide to the minimal and recommended system requirements for running the SDXL model, helping you make informed decisions and ensuring a seamless experience with this powerful AI tool.

4GB VRAM – Absolute Minimal Requirement

Minimum Requirement

SDXL, while a remarkable tool, demands a minimum of 4GB VRAM to function. At this level, it becomes imperative to opt for lightweight software like ComfyUI to ensure a smoother experience. While the base model will technically operate on a 4GB graphics card, our tests indicate that it might push the limits, leading to suboptimal performance.

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6GB VRAM – Better, But Still Not Ideal

Better, but Still Not Ideal

With 6GB graphics card, SDXL performs better than on a 4GB card, but it may still fall short of providing a truly comfortable working experience. Are you prepared to wait up to an hour for the generation of a single 1024×1024 image?


8GB VRAM – A Balanced Choice

A Step in the Right Direction

Users have reported that SDXL operates admirably on an 8GB graphics card. Image generation becomes notably faster, but some complaints persist, particularly from users dealing with Automatic1111. Even so, one generation takes approximately half a minute on a base model with a refiner.

12GB VRAM – The Optimal VRAM

Recommended VRAM for Optimal Performance

For an optimal experience with SDXL, we recommend a graphics card boasting 12GB VRAM. At this level, image generation is swift, taking around 20 seconds per 1024×1024 image with the refiner. Some users have even managed to train a 1024×1024 LORA model on 12GB VRAM.

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16GB VRAM – Speed and Efficiency

Enhanced Performance and Speed

With 16GB VRAM, you can guarantee a comfortable experience while generating 1024×1024 images using the SDXL model with the refiner. It surpasses the 12GB VRAM experience in terms of speed, and if you generate images in batches, the results are even more impressive.

Here’s the 5 best graphics cards with 12 GB VRAM in 2023, along with some key information about each card:

RankGraphics CardGPU NameCUDA Core CountRT Core CountVRAMTDPPrice
1Nvidia Geforce RTX 3060GA1063,5842812 GB GDDR6170W$350
2AMD Radeon RX 6700 XTNavi 222,5604012 GB GDDR6230W$339
3Nvidia Geforce RTX 4070AD1045,8884612 GB GDDR6X200W$599
4Nvidia Geforce RTX 3080 TiGA10210,2408012 GB GDDR6X350W$899
5Nvidia Geforce RTX 4070 TiAD1047,6806012 GB GDDR6X285W$799

Please note that prices may vary, and this list is subjective, reflecting the opinions of the writer. These graphics cards offer various performance levels and price points, catering to different gaming needs and budgets.

24GB VRAM – For Advanced Users

For Advanced Users and Training

If you’re looking to fine-tune models and undertake LoRA training, 24GB VRAM is your ally. Our tests indicate that it should take no more than an hour and a half to complete one training session. Image generation becomes a matter of seconds with this hardware.

Here’s a the best graphics cards with 24GB VRAM

RankGraphics CardVRAMPrice (MSRP)Power
1GeForce RTX 409024GB$1,599 ($1,600)311W
2AMD Radeon RX 7900 XTX24GB$1,000355W

Please note that some information, such as the price and availability, may have changed since the article’s publication, and it’s always a good idea to check the latest prices and reviews before making a purchase decision.

CPU for Stable Diffusion – Minimal Requirements

Match Performance to Your Graphics Card

While there are no specific CPU requirements for SDXL, it’s essential to ensure that your CPU complements the performance of your chosen graphics card. Avoid bottlenecks and aim for a harmonious balance between these two crucial components.

RAM for Stable Diffusion – The More, the Better

Don’t Skimp on Memory

During our rigorous tests, we discovered that working with SDXL with less than 32GB of RAM can prove to be a challenging and uncomfortable experience. To ensure smooth and efficient operation, we strongly recommend having at least 32GB of RAM at your disposal. If you can, consider upgrading to even more RAM for enhanced performance.

Compatibility Beyond Nvidia: AMD, Intel, and Apple M1

Exploring Your Options

While Nvidia GPUs are the preferred choice for SDXL, it’s worth noting that compatibility exists beyond this realm.

AMD Graphics Cards: Community members on Reddit have confirmed that SDXL can function with AMD graphics cards (such as RX 6700 and RX 6800) using Automatic1111. However, expect slower performance and increased SSD writes. For a more efficient experience, consider platforms like Google Colab or other services like it.

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Intel Graphics Cards: Yes, SDXL can technically run on Intel graphics cards. But be warned, it’s far from an ideal scenario. With free alternatives like Google Colab, Clipdrop, and Discord bot available, it’s advisable to steer clear of this path.

Apple M1 Processors: While possible, running SDXL on M1 Macs is not recommended. Popular software like Automatic1111 is optimized for Windows PCs with Nvidia GPUs. Attempting to use it on an M1 Mac, especially with high-quality upscaling like hires.fix upscaler, can result in painstakingly slow performance. Instead, consider utilizing software like DiffusionBee, specifically designed for M1/M2 chips, and ensure you’re using an FP16 model for the best results.

Running SDXL Without a GPU

Consider Your Options Carefully

While it’s theoretically possible to run SDXL without a GPU, we strongly advise against it unless you’re using cloud-based platforms like Google Colab or similar services. Running it locally without a dedicated GPU can lead to unsatisfactory performance.


ensuring that your hardware aligns with the recommended system requirements for SDXL is crucial to unlocking its full potential. By choosing the right graphics card, CPU, and RAM, you can harness the power of Stable Diffusion XL for your AI and machine learning endeavors, ensuring a smoother and more productive experience. Remember, your hardware is the backbone of your AI journey, so invest wisely to reap the rewards. Happy computing!

Please note that these recommendations are based on the information available at the time of writing and may be subject to change as new hardware and software developments emerge. Always ensure you check the latest compatibility and system requirements before making hardware decisions for your SDXL setup.

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How much VRAM do I need?

You need at least 8GB VRAM for good speed. 12GB or more is best.

What is the best video card to buy?

Get an Nvidia RTX 30 series or AMD RX 6000 series card with 12GB+ VRAM.

Can I use my old video card?

Old cards often don’t have enough power. A new card works better.

Does it work on Intel integrated graphics?

No, Intel graphics are too weak. Get a dedicated Nvidia or AMD video card.

How much RAM do I need?

We recommend at least 32GB RAM for good performance.

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Last modified: January 30, 2024

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