AI models that fit on a 288 GB GPU

Fits as published

Models whose published weights, plus the flat overhead, fit this much memory with no quantization.

Fits at FP8

Models that fit only after quantizing the weights to 8 bits (1 byte per parameter). Needs an FP8 checkpoint or an engine that quantizes on load, and a GPU with FP8 support.

Fits at INT4

Models that fit only after quantizing to 4 bits (0.5 byte per parameter). Needs a quantized checkpoint actually published for the model; check its Hugging Face page before relying on a row.

How these numbers are computed

Required VRAM is the weight size at each precision times a flat 1.2 overhead, the same figure every model page shows. It does not include a long context: the KV cache grows with every token, so a model near the top of a tier can need the next one at long context. Read how much VRAM you need for LLMs for the method, the VRAM and quantization glossary entries for the terms, and the VRAM calculator to size a model that is not listed.

Looking for a model by job rather than by memory? Start with coding, reasoning, chat and assistants, vision-language or see the full models directory.

Other VRAM tiers

• 8 GB
• 12 GB
• 16 GB
• 24 GB
• 32 GB
• 48 GB
• 80 GB
• 96 GB
• 141 GB
• 192 GB
• 288 GB