152 open-source AI models you can run on your own infrastructure. Self-hosting gives you complete data privacy, zero per-token costs, and full control over the model and its behavior.
Your data never leaves your infrastructure. Critical for healthcare, finance, legal, and government use cases where data residency and privacy regulations apply.
After the initial hardware investment, there are no per-request charges. At high volumes, self-hosting can be 10-100x cheaper than API-based services.
Run models with vLLM, Ollama, text-generation-inference, or llama.cpp. Most can run on consumer GPUs (RTX 4090) for smaller models, or cloud GPUs (A100, H100) for larger ones.
Self-hosted models can be fine-tuned on your own data, creating domain-specific versions that outperform general-purpose models for your use case.
Self-hosting gives you complete data privacy (no data leaves your servers), eliminates per-token API costs, removes rate limits, enables offline operation, and allows fine-tuning for your specific use case.
Requirements depend on model size. Small models (7B parameters) run on consumer GPUs with 8GB VRAM. Medium models (13-30B) need 24GB+ VRAM. Large models (70B+) require multiple high-end GPUs or specialized inference hardware.
Popular tools include Ollama (easiest setup), llama.cpp (most efficient for CPU inference), vLLM (fastest for GPU serving), and text-generation-webui (feature-rich UI). Each excels at different use cases.
For high-volume usage (thousands of requests/day), self-hosting is significantly cheaper. For low-volume or sporadic use, API access is more cost-effective since you avoid hardware costs and maintenance overhead.