Hardware
How Much VRAM Do You Need for Local AI?
VRAM is one of the first constraints local AI builders hit, but it is not the only one.
Editorial review
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Who this is for
Anyone planning a local AI workstation or deciding which model sizes to test.
Recommended stack
- Small 3B-8B models for low VRAM
- 14B-32B models for stronger local chat
- Server or hosted inference for large MoE models
Model size is only part of it
Context length, KV cache, batch size, quantization, and runtime all affect memory use.
Start with practical tiers
Low-VRAM systems should test small models first. 16GB to 24GB systems can test more capable quantized models. Large models need server-class setups.
Practical recommendations
- Check model cards and quantization notes
- Test your real context length
- Avoid buying hardware for one benchmark headline
Tradeoffs
Aggressive quantization can make models fit, but quality, speed, and context length can change.
Related links
FAQ
Can I run local AI without a GPU?
Yes for smaller quantized models, but responses are usually slower.
Sources
Next steps
Use the model and tool directories to choose the concrete pieces for your local AI stack, then move into stack recipes or related guides when you are ready to build.