llama.cpp vs Ollama
| Feature | llama.cpp | Ollama |
|---|---|---|
| Category | Embeddable | LLMs & AI Infra |
| Sub-category | LLM Runtime | LLM Serving |
| Maturity | stable | stable |
| Complexity | advanced | beginner |
| Performance tier | medium | medium |
| License | MIT | MIT |
| License type | permissive | permissive |
| Pricing | fully free | fully free |
| GitHub stars | 72.0K | 110.0K |
| Contributors | 800 | 500 |
| Commit frequency | daily | daily |
| Plugin ecosystem | none | medium |
| Docs quality | good | good |
| Backing org | Georgi Gerganov | Ollama Inc |
| Funding model | community | vc_backed |
| Min RAM | 2 GB | 4 GB |
| Min CPU cores | 1 | 2 |
| Scaling pattern | single_node | single_node |
| Self-hostable | Yes | Yes |
| K8s native | No | No |
| Offline capable | Yes | Yes |
| Vendor lock-in | none | none |
| Languages | C, C++ | Go, C++ |
| API type | SDK | REST |
| Protocols | HTTP | HTTP |
| Deployment | source, binary | binary, docker |
| SDK languages | c, c++, python, javascript, go, rust, swift | python, javascript, go, rust |
| Team size fit | solo, small, medium | solo, small, medium |
| First release | 2023 | 2023 |
| Latest version | — | — |
When to use llama.cpp
- ✓ Run LLMs on CPU without GPU
- ✓ Embed AI in desktop/mobile apps
- ✓ Quantized model inference for edge devices
- ✓ Backend for Ollama and other wrappers
When to use Ollama
- ✓ Run LLMs locally for private/offline AI
- ✓ Development environment with local AI models
- ✓ Code completion backend for Continue/Tabby
- ✓ Chatbot prototype without API costs
llama.cpp anti-patterns
- ✕ C++ knowledge needed for embedding
- ✕ Model loading time for large models
- ✕ Less user-friendly than Ollama
Ollama anti-patterns
- ✕ Not for high-throughput production serving
- ✕ Single-user optimized not multi-tenant
- ✕ No built-in batching or queuing
- ✕ Needs decent GPU for large models