Top 5 Local LLMs You Should Be
Running in 2025

Meta's Llama 3.1 series raised the bar for open-weight models, and the 8B variant hits the sweet spot between capability and hardware requirements. Pair it with Ollama and you get a dead-simple `ollama run llama3.1` that just works — no Python environment juggling, no quantization headaches.

Mistral AI punched above its weight class when they dropped this model, and it still earns its keep. The instruction-tuned variant follows prompts precisely and responds fast — noticeably faster than most 7–8B competitors at the same hardware budget. Query it hundreds of times a day without hitting a rate limit.

Microsoft's Phi series proved that model size isn't everything. Phi-3 Mini was trained on carefully curated "textbook quality" data, giving it reasoning ability that embarrasses models twice its size. Runs comfortably on a laptop GPU or even a beefy CPU.

Google's Gemma 2 benefits from Google's training infrastructure in a way you can feel. The 9B variant is the one to use — the quality jump from 2B is significant. Particularly strong at structured output and follows complex, multi-step instructions reliably without constant babysitting.

Alibaba's Qwen2 deserves more attention in Western dev circles. One of the strongest 7B-class models for non-English text, and benchmark scores are competitive across the board in English too. Rare 128K context window at this size — process long documents in a single pass.