Self-hosted inference
The practice of running and managing AI models on your own servers or local hardware, rather than sending data to external providers like OpenAI.
What it is
Self-hosted inference involves downloading open-weight models and running them using engines like vLLM. Organizations do this to maintain strict data sovereignty, avoid vendor lock-in, and bypass the unpredictable rate limits and latency fluctuations of public AI APIs.
When you would use it
You mandate self-hosted inference when handling highly sensitive medical, financial, or defense data that legally cannot be transmitted to third-party cloud AI providers.
Common operations
- Deploying a proprietary Llama model securely within a corporate firewall.
- Ensuring zero customer data leaves the internal network.
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