# What is the best local LLM?

The best local LLM for most people in 2026 is a mid-size open model (7B–14B) via Ollama — pick by RAM and use case.

The best local LLM is the one that fits your hardware and task — not the biggest model you can barely load. Use Ollama's library and the Open LLM Leaderboard as starting points, then test on your own prompts.

General chat: Llama 3.2, Mistral, or Qwen 2.5 (7B–14B).

Coding: Qwen 2.5 Coder or DeepSeek Coder.

Low RAM (8 GB): Phi-3 or Llama 3.2 3B — see Microsoft Phi model family for small-model design goals.

High-end workstation (32 GB+ VRAM): 32B–70B quantizations for near-cloud quality per llama.cpp quantization support.

Install Ollama, try two models on real work, and keep the one with the best speed-to-quality ratio.



## Sources
1. [Ollama model library](https://ollama.com/library)
2. [Hugging Face — Open LLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
3. [Ollama — Llama 3.2](https://ollama.com/library/llama3.2)
4. [Ollama — Mistral](https://ollama.com/library/mistral)
5. [Ollama — Qwen 2.5](https://ollama.com/library/qwen2.5)
6. [Ollama — Qwen 2.5 Coder](https://ollama.com/library/qwen2.5-coder)
7. [DeepSeek Coder V2 Lite](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct)
8. [Ollama — Phi-3](https://ollama.com/library/phi3)
9. [Microsoft — Phi models](https://azure.microsoft.com/en-us/products/phi)
10. [llama.cpp](https://github.com/ggerganov/llama.cpp)

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Page: https://localizedaiguide.com/q/best-local-llm