Overview
Deploy Score is a 0-1.0 metric that measures how easily an AI model can be deployed and run locally or in production.
Score Formula (V4.4)
Deploy Score =
0.25 Γ GGUF availability +
0.25 Γ Ollama availability +
0.20 Γ Context length factor +
0.15 Γ Size factor +
0.15 Γ Quantization formats
Factors Explained
GGUF Availability (+25%)
Models with GGUF quantized versions are much easier to run locally using llama.cpp, Ollama, or LM Studio.
Ollama Support (+25%)
If a model is available in the Ollama library, deployment is as simple as:
ollama run model-name
Context Length (+0-20%)
Longer context = more useful, but also harder to run:
- 32K+: +20%
- 8K-32K: +15%
- 4K-8K: +10%
- <4K: +5%
Model Size (+5-15%)
Smaller models are easier to deploy:
- <10B: +15%
- 10-40B: +10%
- 40B+: +5%
Quantization Formats (+0-15%)
More quantization options = more flexibility:
- Q4, Q5, Q8, FP16, etc.
Score Interpretation
| Score | Deployment Ease |
|---|---|
| 0.8+ | Excellent - One-click deploy |
| 0.5-0.8 | Good - Some setup required |
| 0.3-0.5 | Moderate - Technical knowledge needed |
| <0.3 | Complex - Significant resources required |