llama.cpp vs private-gpt
Side-by-side comparison of two AI agent tools
Short answer
- llama.cpp is growing faster: +4,848 GitHub stars in the last 30 days vs +57 for private-gpt.
- Pick llama.cpp for: lLM inference in C/C++. Pick private-gpt for: interact with your documents using the power of GPT, 100% privately, no data leaks.
From GitHub data refreshed daily.
llama.cppopen-source
LLM inference in C/C++
private-gptopen-source
Interact with your documents using the power of GPT, 100% privately, no data leaks
Metrics
| llama.cpp | private-gpt | |
|---|---|---|
| Stars | 130.1k | 57.6k |
| Star velocity /mo | 4.8k | 56.82539682539682 |
| Commits (90d) | 1.5k | 62 |
| Releases (6m) | 10 | 4 |
| Overall score | 0.9215106254372528 | 0.5500380972578883 |
Pros
- +High-performance C/C++ implementation optimized for local inference with minimal resource overhead
- +Extensive model format support including GGUF quantization and native integration with Hugging Face ecosystem
- +Multiple deployment options including CLI tools, REST API server, Docker containers, and IDE extensions
- +Complete data privacy with 100% local processing and no external data transmission
- +Production-ready with comprehensive API following OpenAI standards and streaming support
- +Flexible architecture offering both high-level RAG pipeline and low-level API for custom implementations
Cons
- -Requires technical knowledge for compilation and model conversion processes
- -Limited to inference only - no training capabilities
- -Frequent API changes may require code updates for downstream applications
- -Requires significant local compute resources to run LLMs effectively
- -Setup complexity may be challenging for non-technical users
- -Limited to documents that can be processed and stored locally
Use Cases
- •Local AI inference for privacy-sensitive applications without cloud dependencies
- •Code completion and development assistance through VS Code and Vim extensions
- •Building AI-powered applications with REST API integration via llama-server
- •Enterprise document analysis for regulated industries requiring complete data privacy
- •Offline research and document querying in environments without internet connectivity
- •Building custom AI applications with contextual document understanding without cloud dependencies
FAQ
- Which is more popular, llama.cpp or private-gpt?
- llama.cpp has more GitHub stars (130,128 vs 57,562).
- Which is more actively developed, llama.cpp or private-gpt?
- llama.cpp had more commits in the last 90 days (1,491 vs 62).
- Should I use llama.cpp or private-gpt?
- Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.