To evaluate SSD performance in real-world AI workloads, we tested the load times of various deep learning models. These include both image classifiers (such as ResNet, VGG19, and EfficientNet) and a large language model (LLaMA 2 7B). Since model sizes and complexities vary widely, we calculated the geometric mean across all tests to provide a clear and balanced comparison. The chart below gives an at-a-glance view of which drives handle AI-related loading tasks most efficiently. The chart above presents the average of all our comparisons, including tests with LLMs (Large Language Models) such as LLaMA 2 7B, as well as various image classification models that will be detailed below. Individual Test Results Large Language Models To evaluate real-world AI performance, we use a benchmark that measures the time it takes to load the LLaMA 2 7B large language model from SSD storage into system and GPU memory. The procedure replicates a typical machine learning workflow, where models need to be initialized quickly for inference or fine-tuning. Using the Hugging Face Transformers library in offline mode ensures the model is loaded entirely from local storage, without network interference. The tokenizer and full model are preloaded using PyTorch in float16 precision to simulate a realistic deployment scenario, and the total loading time—from disk to memory—is recorded. By running the benchmark test across multiple SSDs, we can identify which ones deliver the fastest model initialization times, a critical factor for AI tasks that demand low startup latency. Image Classification Models Benchmark Our benchmark measures the loading time of large image classification models by analyzing two stages: transfer from SSD to system memory, and then to the GPU. Each model is loaded 20 times to calculate an average and standard deviation for reliable results. The goal is to compare loading efficiency across architectures—particularly important