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Stable Diffusion Inference Benchmark

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Summary

This article discusses how SaladCloud, a distributed cloud computing environment, ran a benchmark to evaluate the use of consumer-grade GPUs for generative AI inference at scale. The benchmark used a SaaS-style, generative AI image generation tool and ran on 750 unique nodes with at least 4 vCPUs, at least 8GB of RAM, and a NVIDIA RTX 2000, 3000, or 4000 series GPU with at least 8GB of VRAM. Over the 24 hour period, it processed a total of 9,274,913 image generation requests producing 3.62 TB of content, with an average image generation cycle time of 7 seconds. The benchmark showed that generative AI inference at-scale on consumer-grade GPUs is practical, affordable, and a path to lower cloud costs. The article also details the application architecture, deployment on SaladCloud, and the results of the benchmark. Additionally, it discusses the potential of SaladCloud and the advantages of using consumer-grade GPUs for generative AI inference, as well as provides resources for those interested in trying SaladCloud.

Q&As

What are the benefits of using consumer-grade GPUs for Stable Diffusion inference at scale?
The benefits of using consumer-grade GPUs for Stable Diffusion inference at scale include being practical, affordable, and a path to lower cloud costs.

What was the total cost of generating 9.2 million images in 24 hours?
The total cost of generating 9.2 million images in 24 hours was $1,872.

How was the application architecture for the image generation structured?
The application architecture for the image generation was structured with a web-based application (frontend and backend), a dedicated job queue, an inference container, and a block storage service.

What model was used for the image generation?
The model used for the image generation was Automatic1111's Stable Diffusion Web UI.

How can people use SaladCloud to run their own models or pre-configured recipes?
People can use SaladCloud to run their own models or pre-configured recipes by checking out the SaladCloud Portal for a free trial and getting $5 of free credits.

AI Comments

👍 This benchmark shows that generative AI inference at-scale on consumer-grade GPUs is practical, affordable, and a path to lower cloud costs. The results of the benchmark were impressive, achieving 9.2 million images generated in 24 hours.

👎 The benchmark was not optimized, and there are a number of technical tasks that could be undertaken to improve the performance. The images produced were also fixed to a size of 512x512 pixels, which may not be suitable for some applications.

AI Discussion

Me: It's about a benchmark for stable diffusion inference on consumer-grade GPUs. They ran a fine-tuned, Stable Diffusion-based application on SaladCloud and were able to generate over 9 million images in 24 hours for $1872. The article goes on to discuss the architecture of the system, deployment on SaladCloud, and the results of the benchmark.

Friend: Wow, that's impressive! It looks like consumer-grade GPUs are not only capable of running Stable Diffusion inference at scale, but they are also more cost-effective. This could be a great way to reduce cloud costs for businesses.

Me: Absolutely! It's exciting to see how generative AI is becoming more accessible and affordable. Plus, SaladCloud provides low prices and on-demand availability of GPUs, making it easy to scale up or down quickly. It looks like a great option for businesses.

Action items

Research and compare the cost of running inference at scale on consumer-grade GPUs versus enterprise-grade GPUs.
Experiment with different parameters and configurations to optimize the image generation process.
Sign up for a free trial of SaladCloud to explore the potential of using consumer-grade GPUs for generative AI inference.

Technical terms

Salad Container Engine (SCE): A container engine developed by Salad Technologies that is used to run applications on SaladCloud.
Salad Gateway Service (SGS): A gateway service developed by Salad Technologies that provides access to SaladCloud.
Generative A.I.: Artificial intelligence that can create new content, such as paintings, music, and writing.
Azure Queue Storage: A cloud-based queue storage service provided by Microsoft Azure.
Azure Blob Storage: A cloud-based block storage service provided by Microsoft Azure.
LoRA: A text-to-image generation model developed by Automatic1111.
Euler Ancestral: A type of sampler used in the Stable Diffusion model.
CFG Scale: A parameter used in the Stable Diffusion model that determines the complexity of the generated image.
SaladCloud: A distributed cloud computing environment made up of the world's most powerful gaming PCs.