di37/running-llms-locally
A comprehensive guide for running Large Language Models on your local hardware using popular frameworks like llama.cpp, Ollama, HuggingFace Transformers, vLLM, and LM Studio. Includes optimization techniques, performance comparisons, and step-by-step setup instructions for privacy-focused, cost-effective AI without cloud dependencies.
101
Stars
12
Forks
29.2
Radar Score
Declining
Momentum
Scoring Breakdown
Impact31.6
Velocity18.3
Health40.5
Growth Metrics
7-Day Stars
+0
30-Day Stars
+0
Acceleration
x
Language
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🛡️ Enterprise Readiness & Commercial Safety
Organic Growth
69%
Moderate Confidence
License
Permissive (Commercial Friendly)
Safe for commercial use, modification, and distribution.
Health Grade
B
Based on maintenance, issue response, and release cadence.
Radar Score
29.2
Composite of Impact, Velocity, and Health.
⚠️ Growth Anomaly Detected:
- Issue engagement: 0 open issues for 101 stars (0.0%).
Disclaimer: This is an automated heuristic assessment based on public GitHub signals. Always perform your own security audit, dependency review, and legal compliance check before production deployment.
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