Open vs Closed Weights — Understanding AI Models

The open vs closed distinction affects what you can do with a model. Understanding the differences helps you choose models based on legal and technical constraints.

What "open weights" means:

Open weights models allow you to:

Download and run the model locally
Fine-tune on your own data
Modify the weights
Deploy for commercial use (varies by license)

This does not mean the training data is public, training code is shared, or the model is free to use in all contexts.

Common licensing scenarios:

License	Models	Commercial use	Modification	redistribution
Apache 2.0	Llama 3.1	Yes	Yes	Yes
Llama 2	Llama 2	Yes (with restrictions)	Yes	No
CC BY-NC	Some research	No	Yes	No
Custom	Most closed models	No	No	No

What "closed weights" means:

Closed models do not release weights. You can only access them through APIs or hosted services. You cannot:

Run locally
Inspect weights
Fine-tune (typically)
Deploy on your own infrastructure

The quality/efficiency tradeoff:

Closed models (GPT-4, Claude, Gemini) are generally ahead in capability because:

Larger training compute budgets
More sophisticated alignment
Continuous improvement without model exposure

Open models catch up but lag by 6-12 months on average.

Running open models locally:

# Typical local inference setup
# 1. Download model
huggingface-cli download meta-llama/Meta-Llama-3.1-8B-Instruct-GGUF

# 2. Run with llama.cpp
./llama-cli -m ./llama-3.1-8b-instruct-q4_k_m.gguf \\
    -n 2048 \\
    -ctx 8192 \\
    -p "You are a helpful assistant."

# 3. Monitor resource usage
nvidia-smi

When open weights matter:

Privacy-sensitive data (medical, legal, financial)
Cost optimization for high-volume inference
Custom fine-tuning requirements
Offline operation requirement