Three Models, Three Sizes

This reading lists the three Lab 1 models and the memory each one needs to load.

The Three Models

In Lab 1, pane 1 was Ministral 3B, pane 2 was Ministral 14B, and pane 3 was Mistral Large 3. Ministral 3B has 3.4 billion parameters for language, and its model card says it fits in 8 GB of GPU memory. Ministral 14B has 13.5 billion parameters for language, and its model card says it fits in 24 GB of GPU memory. Mistral Large 3 has 675 billion parameters in total. Its model card says one form of it runs on a single machine with eight NVIDIA H200 GPUs, and each H200 has 141 GB of memory. That machine therefore has 1,128 GB of GPU memory, because 8 times 141 GB is 1,128 GB.

Deciding Whether a Model Fits

A model fits on a machine when the machine's memory is at least as large as the memory the model needs. Here, the GPU memory on a model card counts as the memory the model needs. The number that decides it is the memory, not the storage, because a parameter file in storage has not been loaded yet. For example, a typical school Chromebook has 4 GB or 8 GB of memory and 32 GB or 64 GB of storage. Its GPU has no memory of its own and shares the system memory with everything else that is running, including ChromeOS and the browser. Ministral 3B, the smallest of the three models, fits in 8 GB of GPU memory. The 4 GB Chromebook has less than 8 GB, so the model does not fit. The 8 GB Chromebook has exactly 8 GB, but ChromeOS uses part of that memory, so the model does not fit there. The 64 GB of storage does not change the answer, because the computer has to load the parameter file into memory before the model can run. Mistral Large 3 does not fit on the example machine in Figure 1, because the 24 GB on its RTX 4090 is far less than the 1,128 GB in the machine the model card names.

Sources: Mistral AI, model cards for Ministral 3 3B, Ministral 3 14B and Mistral Large 3 (2025); NVIDIA, H200 and GeForce RTX 4090 specification pages; Lenovo, 100e Chromebook Gen 4 PSREF (2023); Verma and Vaidya, "Mastering LLM Techniques: Inference Optimization," NVIDIA (2023); OpenStax, Introduction to Computer Science (2024); MuggsOfCompSci, Lab 1 records (2026).