Top 7 Open Source LLMs You Can Run for Free in 2025

The open-source AI ecosystem has reached a point where you can run genuinely impressive language models for free on your own hardware. Here are the seven best options available in 2025, selected for capability, accessibility, and license terms.

1. Llama 3 (Meta)

Available in 8B, 70B, and 405B. The 70B variant is extraordinary for an open-source model. Our Llama 3 vs Mistral comparison breaks down exactly where each model wins. License: Llama Community License (commercial use allowed with restrictions).

2. Mistral 7B / Mixtral 8x7B (Mistral AI)

Mistral 7B is the best-in-class 7B model for reasoning and instruction following. Mixtral 8x7B delivers 70B-level quality at significantly lower inference cost. License: Apache 2.0 — the most permissive commercially.

3. Gemma 2 (Google)

Google’s open model family (2B and 9B variants) is optimized for efficient inference on consumer hardware. Gemma 2 9B competes well with Llama 3 8B while running faster on CPU-only setups. License: Gemma Terms of Use. Run it via the Ollama setup guide.

4. Phi-3 (Microsoft)

Microsoft’s Phi-3 Mini (3.8B parameters) outperforms many 7B models on reasoning benchmarks, making it ideal for edge deployment. License: MIT. Available on Hugging Face with comprehensive model cards.

5. Qwen2 (Alibaba)

Qwen2 72B is one of the strongest open models for multilingual tasks, code generation, and math. Available in sizes from 0.5B to 72B. Particularly valuable for applications requiring strong Chinese language support.

6. DeepSeek-Coder V2

DeepSeek’s models have garnered attention for exceptional coding ability, rivaling proprietary models on coding benchmarks at a fraction of the cost to run. License: DeepSeek License (commercial use with attribution).

7. Command R+ (Cohere)

Cohere’s open-weight Command R+ is optimized for retrieval-augmented generation (RAG) — purpose-built for knowledge base queries and document Q&A.

Getting Started

All of these models can be fine-tuned on custom datasets using open-source tools like LoRA and QLoRA. For most users, the fastest path to running any of these is via Ollama. For the broader context of how these fit against commercial options, our guide to AI tools in 2025 covers the full landscape.