Local AI model families
Model-family reference pages with explicit caveats, source-backed framing, and no unsupported performance claims.
Choose by task, size, and fit
Model-family pages are not benchmark pages. They are orientation records that help beginners understand the names they see in Ollama, LM Studio, Hugging Face, and local model catalogs.
Do not choose a model by brand name alone. Choose by task, size, quantization, context, license, and hardware fit.
- Use the RAM/VRAM calculator before downloading a model.
- Read the 8GB, 16GB, and 32GB pages for realistic memory guidance.
- Check the model source and license before using a model for business work.
Llama
Do not choose a local model only because the family name is popular. Choose by task, parameter size, quantization, context, license, runtime support, and hardware fit. A small model that runs smoothly is a better first experience than a large model that barely loads.
Llama is a model-family orientation page, not a benchmark page. Use it to explain what the family is commonly investigated for, then route readers to hardware sizing and exact model records before they download anything.
Qwen
Do not choose a local model only because the family name is popular. Choose by task, parameter size, quantization, context, license, runtime support, and hardware fit. A small model that runs smoothly is a better first experience than a large model that barely loads.
Qwen is a model-family orientation page, not a benchmark page. Use it to explain what the family is commonly investigated for, then route readers to hardware sizing and exact model records before they download anything.
Mistral
Do not choose a local model only because the family name is popular. Choose by task, parameter size, quantization, context, license, runtime support, and hardware fit. A small model that runs smoothly is a better first experience than a large model that barely loads.
Mistral is a model-family orientation page, not a benchmark page. Use it to explain what the family is commonly investigated for, then route readers to hardware sizing and exact model records before they download anything.
Gemma
Do not choose a local model only because the family name is popular. Choose by task, parameter size, quantization, context, license, runtime support, and hardware fit. A small model that runs smoothly is a better first experience than a large model that barely loads.
Gemma is a model-family orientation page, not a benchmark page. Use it to explain what the family is commonly investigated for, then route readers to hardware sizing and exact model records before they download anything.
Phi
Do not choose a local model only because the family name is popular. Choose by task, parameter size, quantization, context, license, runtime support, and hardware fit. A small model that runs smoothly is a better first experience than a large model that barely loads.
Phi is a model-family orientation page, not a benchmark page. Use it to explain what the family is commonly investigated for, then route readers to hardware sizing and exact model records before they download anything.
DeepSeek-style local models
Do not choose a local model only because the family name is popular. Choose by task, parameter size, quantization, context, license, runtime support, and hardware fit. A small model that runs smoothly is a better first experience than a large model that barely loads.
DeepSeek-style Local Models is a model-family orientation page, not a benchmark page. Use it to explain what the family is commonly investigated for, then route readers to hardware sizing and exact model records before they download anything.