Best Hugging Face models for an 8GB GPU

An 8GB graphics card can run useful open models, but the repository name alone does not tell you whether the selected weights, context and runtime will fit.

Quick answer

Start with smaller language, embedding, classification or compact image models. For generative LLMs, 4-bit quantization is often necessary once parameter counts move beyond the smallest families. Leave memory for the runtime, KV cache and input—not just weights.

Live 8GB shortlist

The models below are currently estimated by AI Pentium to require no more than 8GB of minimum VRAM. They are ordered by Hugging Face download metadata and update automatically with the catalog.

What “fits in 8GB” really means

• The exact checkpoint and precision matter: FP16, INT8 and 4-bit weights have very different footprints.
• Long context and larger batches increase memory use, especially for text generation.
• Image resolution, frame count and batch size drive activation memory for image and video models.
• Runtime implementations allocate different kernels, caches and temporary buffers.

How to verify before downloading

1. Open the model detail page and note the parameter count and estimated minimum VRAM.
2. Follow the source link to inspect the exact files and supported quantizations.
3. Begin with batch size one and a conservative context or image size.
4. Measure peak allocated memory, then increase the workload gradually.

This list is a discovery aid, not a benchmark or guarantee. Always test the exact Hugging Face repository and runtime.