Choosing the Right Open Source AI Model in Ollama: A Practical Guide

The open source AI landscape has exploded, with powerful models now accessible to anyone running Ollama. From compact models that fit on a laptop to research grade giants requiring server clusters, the choices can feel overwhelming. This guide provides a structured overview of the most popular families Llama, Gemma, QwQ, DeepSeek, Phi, Mistral and more helping you choose the right one for your specific goals.

1. Why Model Choice Matters

Not all large language models (LLMs) are created equal. The right choice depends on:

• Compute Resources: How much RAM/VRAM you have available.
• Use Case: Coding, reasoning, chat, vision tasks, or enterprise AI.
• Performance vs Efficiency: Bigger models perform better but may not be practical for your hardware.

Choosing wisely ensures smooth deployment and better outcomes.

2. Model Families and Their Strengths

Here's a breakdown of the most notable families in Ollama:

Gemma 3 (Google)

• Sizes: 1B - 27B
• Strengths: Lightweight, efficient, well balanced reasoning.
• Best for: On device AI, cost efficient deployment, developers needing solid reasoning without massive GPUs.

QwQ (Qwen by Alibaba)

• Size: 32B (20GB)
• Strengths: Advanced reasoning, strong multilingual capabilities.
• Best for: Global teams, multilingual applications, deeper logic tasks.

DeepSeek-R1

• Sizes: 7B and 671B (!!)
• Strengths: Ambitious reasoning power, with the 671B model among the largest ever released.
• Best for: Research, enterprise clusters, cutting edge experimentation. Not practical for laptops or single GPU setups.

Llama (Meta)

• Variants: 3.1, 3.2, 3.3, and 4 (1B - 400B)
• Strengths: General purpose, reliable, state of the art for reasoning and multimodal (Vision) tasks.
• Best for: Versatile use coding, chatbots, enterprise AI, and image understanding.

Phi 4 (Microsoft)

• Sizes: 3.8B - 14B
• Strengths: High reasoning power relative to size; optimized for efficiency.
• Best for: Lightweight assistants, reasoning heavy workflows with limited resources.

Mistral

• Size: 7B
• Strengths: Compact, fast, widely adopted, strong community support.
• Best for: General purpose chat, coding help, smaller deployments.

Other Notables

• Moondream: Tiny, edge ready model (1.4B), good for basic tasks on minimal hardware.
• Code Llama: Specialized for programming and developer workflows.
• LLaVA: Vision + Language (multimodal), great for image to text use cases.
• Granite-3.3: IBM's enterprise oriented model, reliable for business applications.

3. Hardware Considerations

The model size dictates the minimum VRAM/system RAM you'll need:

• Under 5GB: Runs on laptops (Gemma 1B/4B, Mistral 7B, Phi 4 Mini).
• 5GB - 24GB: Requires a mid range GPU or Mac M-series chip (Gemma 12B, Llama 3.2 11B, Phi 14B).
• 24GB - 48GB: High-end workstation GPUs (Llama 3.3 70B, QwQ 32B, Gemma 27B).
• 100GB+: Multi GPU setups or enterprise clusters (Llama 4 400B, DeepSeek-R1 671B).

4. Practical Recommendations

If you're unsure where to start:

• For Chat & Productivity: Mistral 7B, Phi 4 Mini, Gemma 4B.
• For Coding: Code Llama 7B, Phi 14B, Llama 3.3 (70B if resources allow).
• For Multimodal: LLaVA 7B, Llama 3.2 Vision (11B - 90B).
• For Enterprise AI: Granite-3.3, QwQ 32B, Llama 4 Maverick (400B).

5. Conclusion

Ollama makes it simple to experiment with world class AI models on your own hardware. The key is choosing the right model for your needs balancing size, performance, and resources. Start small with lightweight models, scale up as your use cases demand, and explore specialized models like Code Llama or LLaVA for targeted workflows.

With the right selection, you can unlock powerful AI capabilities without being locked into a closed ecosystem.