[HN] Ask HN: How to serve inference as we do with containes with cached token

Summary

A user from a private education group is seeking guidance on efficiently serving AI model inference, specifically with cached tokens, to an internal research team using their existing GPUs. They are struggling to navigate the complex and rapidly evolving AI inference stack, despite experimenting with tools like vLLM, to distribute access without dedicating a GPU per user. The core challenge is optimizing GPU utilization for internal model access and scaling.

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