Segment Anything Model (SAM) Benchmark on 22 consumer GPUs

# Benchmarking the Segment Anything Model (SAM)

In this benchmark, we do an unprompted full-image segmentation on 152,848 images from the [COCO 2017](https://www.kaggle.com/datasets/awsaf49/coco-2017-dataset/data) and [AVA](https://www.kaggle.com/datasets/nicolacarrassi/ava-aesthetic-visual-assessment) image datasets. We evaluate inference speed and cost-performance across 302 nodes on [SaladCloud](https://www.salad.com/) representing 22 different consumer GPU classes.

To do this, we created a container group targeting a capacity of 100 nodes, with the “Stable Diffusion Compatible” GPU class. All nodes were assigned 2 vCPU and 8GB RAM. Here’s what we found.

# 50K+ …

benchmark benchmarking capacity class computervision consumer diffusion found gpu gpus images per rtx rtx 3060 sam segment segment anything segment anything model stable diffusion targeting