Ollama Model Library — Best AI Models for Local Use

What it is

This guide covers the best models available on Ollama for local AI development. It compares Llama, Mistral, Gemma, Phi, and Qwen model families across coding, chat, and reasoning tasks, helping you choose the right model for your hardware and use case.

Developers and researchers who run AI models locally using Ollama can use this guide to skip the trial-and-error of testing every model and go straight to the ones that perform well for their specific needs.

How it saves time or tokens

Downloading and testing every model in Ollama's library takes hours. This guide distills the comparison into practical recommendations by use case (coding, chat, reasoning) and hardware tier (8GB, 16GB, 32GB+ RAM). You save the time spent on models that do not fit your constraints.

How to use

1. Check your available RAM and GPU VRAM to determine your hardware tier.
2. Identify your primary use case (coding assistance, conversational chat, or analytical reasoning).
3. Pull the recommended model from the guide using ollama pull.

Example

# Pull a coding-focused model
ollama pull codellama:13b

# Pull a general chat model
ollama pull llama3.1:8b

# Pull a reasoning model
ollama pull qwen2.5:14b

# Test the model
ollama run llama3.1:8b 'Explain dependency injection in 3 sentences'

# List installed models
ollama list

Related on TokRepo

• Local LLM tools — Compare Ollama with other local model runners like LM Studio and llama.cpp.
• Ollama integration — Deep-dive into Ollama setup, configuration, and optimization.

Common pitfalls

• Pulling the largest model variant without checking RAM requirements. A 70B model needs 40GB+ RAM; start with smaller variants and scale up.
• Assuming all models handle all tasks equally. Coding models like CodeLlama excel at code but underperform at general chat compared to Llama 3.1.
• Not quantizing models for constrained hardware. Ollama offers Q4, Q5, and Q8 quantization variants that trade minor quality for significant memory savings.